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Principles of Remote Sensing

DSMHT-213

Ariful Islam

Department of Disaster Science and Management

Faculty of Earth and Environmental Science

University of Dhaka

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What is Remote Sensing? "Remote sensing is the science (and to some extent, art) of acquiring information about the Earth's surface without actually being in contact with it. This is done by sensing and recording reflected or emitted energy and processing, analyzing, and applying that information." Remote sensing is the means of observing objects and their physical, optical and

geometrical properties without touching these objects. The information is gathered

through electromagnetic waves or lights of different wave length. The light falls on the

object and scatters back to the sensor which is sensitive to the intensity of radiation.

The sensor is photo sensitive and as soon as the light reflects back to the sensor a

voltage is created which produces a signal that is saved in a storage device.

In much of remote sensing, the process involves an interaction between incident radiation and the targets of interest. This is exemplified by the use of imaging systems where the following seven elements are involved. Note, however that remote sensing also involves the sensing of emitted energy and the use of non-imaging sensors. 1. Energy Source or Illumination (A) – the first requirement for remote sensing is to have an energy source which illuminates or provides electromagnetic energy to the target of interest.

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2. Radiation and the Atmosphere (B) – as the energy travels from its source to the target, it will come in contact with and interact with the atmosphere it passes through. This interaction may take place a second time as the energy travels from the target to the sensor. 3. Interaction with the Target (C) - once the energy makes its way to the target through the atmosphere, it interacts with the target depending on the properties of both the target and the radiation. 4. Recording of Energy by the Sensor (D) - after the energy has been scattered by, or emitted from the target, we require a sensor (remote - not in contact with the target) to collect and record the electromagnetic radiation. 5. Transmission, Reception, and Processing (E) - the energy recorded by the sensor has to be transmitted, often in electronic form, to a receiving and processing station where the data are processed into an image (hardcopy and/or digital). 6. Interpretation and Analysis (F) - the processed image is interpreted, visually and/or digitally or electronically, to extract information about the target which was illuminated. 7. Application (G) - the final element of the remote sensing process is achieved when we apply the information we have been able to extract from the imagery about the target in order to better understand it, reveal some new information, or assist in solving a particular problem.

The Electromagnetic Spectrum

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The electromagnetic spectrum can be divided into several wavelength (frequency) regions, among which only a narrow band from about 400 to 700 nm is visible to the human eyes. Note that there is no sharp boundary between these regions. The boundaries shown in the above figures are approximate and there are overlaps between two adjacent regions.

Wavelength units: 1 mm = 1000 µm; 1 µm = 1000 nm.

Radio Waves: 10 cm to 10 km wavelength. The short radio wave is reflected from ionosphere

Microwaves: 1 mm to 1 m wavelength. The microwaves are further divided into different frequency (wavelength) bands: (1 GHz = 109 Hz)

o P band: 0.3 - 1 GHz (30 - 100 cm) o L band: 1 - 2 GHz (15 - 30 cm) o S band: 2 - 4 GHz (7.5 - 15 cm) o C band: 4 - 8 GHz (3.8 - 7.5 cm) o X band: 8 - 12.5 GHz (2.4 - 3.8 cm) o Ku band: 12.5 - 18 GHz (1.7 - 2.4 cm) o K band: 18 - 26.5 GHz (1.1 - 1.7 cm) o Ka band: 26.5 - 40 GHz (0.75 - 1.1 cm)

Infrared: 0.7 to 300 µm wavelength. This region is further divided into the following bands:

Near Infrared (NIR): 0.7 to 1.5 µm. Short Wavelength Infrared (SWIR): 1.5 to 3 µm. Mid Wavelength Infrared (MWIR): 3 to 8 µm. Long Wanelength Infrared (LWIR): 8 to 15 µm. Far Infrared (FIR): longer than 15 µm.

The NIR and SWIR are also known as the Reflected Infrared, referring to the main infrared component of the solar radiation reflected from the earth's surface. The MWIR and LWIR are the Thermal Infrared.

Visible Light: Blue, green, and red are the primary colours or wavelengths of the visible spectrum. They are defined as such because no single primary colour can be created from the other two, but all other colours can be formed by combining blue, green, and red in various proportions. This narrow band of electromagnetic radiation extends from about 400 nm (violet) to about 700 nm (red). The various colour components of the visible spectrum fall roughly within the following wavelength regions:

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o Red: 610 - 700 nm o Orange: 590 - 610 nm o Yellow: 570 - 590 nm o Green: 500 - 570 nm o Blue: 450 - 500 nm o Indigo: 430 - 450 nm o Violet: 400 - 430 nm

Ultraviolet: 3 to 400 nm . Ultraviolet or UV portion of the spectrum has the shortest wavelengths which are practical for remote sensing. This radiation is just beyond the violet portion of the visible wavelengths

X-Rays and Gamma Rays

Description of Electromagnetic Waves

Electromagnetic waves are energy transported through space in the form of periodic disturbances of electric and magnetic fields.

The waves are constitutes by Electric and magnetic vectors. The waves are sinusoidal in nature and is describes mathematically by the following expression.

)sin( tkxAy , where

2k and

T

2 and Tk ,,, are wave number, wave

length, angular frequency and T is the time period. The frequency, f=T

1

All electromagnetic waves travel through space at the same speed, c = 2.99792458 x 108 m/s, commonly known as the speed of light. An electromagnetic wave is characterized by a frequency and a wavelength. These two quantities are related to the speed of light by the equation,

c = f x

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The frequency (and hence, the wavelength) of an electromagnetic wave depends on its source. There is a wide range of frequency encountered in our physical world, ranging from the low frequency of the electric waves generated by the power transmission lines to the very high frequency of the gamma rays originating from the atomic nuclei. This wide frequency range of electromagnetic waves constitutes the Electromagnetic Spectrum.

Blackbody Radiation All material bodies emit radiation. The intensity and wave length of the radiation

depends on the temperature of the body. If the emissivity of the body of the body is 1

then it is known as blackbody. But in reality there is no perfect blackbody, but most of

the objects may be considered very close to blackbody.

Planck's law describes the electromagnetic radiation emitted by a black body in thermal equilibrium at a definite temperature. The law is named after Max Planck, who originally proposed it in 1900. It is a pioneering result of modern physics and quantum theory.

The spectral radiance of a body, , describes the amount of energy it gives off as radiation in different frequencies. It is measured in terms of the power emitted per unit area of the body and per unit solid angle for a frequency μ. Planck showed that the spectral radiance of a body at absolute temperature T is given by

Where kB the Boltzmann constant (1.3806488×10−23 J/K.), h the Planck constant (6.62606957×10−34Js) and c the speed of light in the medium, whether material or vacuum.[1][2][3] The spectral radiance can also be measured per unit wavelength instead of per unit frequency. In this case, it is given by

The peak wavelength of the emission is inversely proportional to its temperature in Kelvin.

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Wien's Law is the equation to use to solve for this:

T

0029.0max Meter, where T is in Kelvin

For earth’s surface with T=303; the wave length with maximum energy for T= 303 K is 9.5 µm.

Atmospheric Interaction of Electromagnetic Waves/ Solar Radiation The earth has a gaseous envelop around its solid and liquid core. The electromagnetic

radiations while passing through the atmosphere interacts with atmospheric

molecules causing scattering. Before radiation used for remote sensing reaches the

Earth's surface it has to travel through some distance of the Earth's atmosphere.

Particles and gases in the atmosphere can affect the incoming light and radiation.

These effects are caused by the mechanisms of scattering and absorption.

Top of the Atmosphere o Solar radiation arrives essentially unchanged by passage through space o Interacts with gas molecules, water droplets and aerosols in the

atmosphere

Absorption o Radiant energy converted into internal energy

Ceases to exist as radiant energy Heats atmosphere directly

o Very little absorption in visible wavelengths Ozone absorbs most ultraviolet CO2, water vapor and water droplets absorb near infrared

Transmission o Sunlight passing through atmosphere without being altered

Light arriving at surface directly from the Sun Makes the disk of the Sun visible Creates patterns of strong light and shadows

Reflection o Interaction that changes direction of light o Reflection from surfaces

Radiation redirected away from surface without being absorbed Reflected light makes objects visible Wavelength-dependent reflection sensed as color differences

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o Specular reflection Coherent beam reflected - images preserved Mirrors, shiny metal and smooth water surfaces

Scattering o Light reflected in all directions - images not preserved o Fogged mirror (water droplets) scatter at different angles, so can't

see anything in mirror o Comparable effect with fog on glass scattering transmitted light o Atmospheric molecules and aerosols scatter incoming solar radiation o Some forward scattered and continues toward surface at different

angle o Some back scattered out to space taking energy out of earth-

atmosphere system

Figure-3 Scattering of solar radiation in the atmosphere in all direction and thus the diffused light is produced (left). The right panel is scattering by clouds

Diffuse radiation o Some light reaches surface after being scattered

Appears to originate from location of last scattering event Illuminates sky outside of direct solar beam No atmosphere on moon, so sky is black except for direct light

source o Amounts of direct and diffuse light reaching surface vary depending on

atmospheric conditions Clear, dry atmosphere - large amount of direct, little diffuse

High contrast between dark shadows and brightly illuminated surfaces

Sky appears deep blue

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o Cloud cover - no direct, all diffuse

Light coming from all directions of the sky, so only shadows directly underneath things

Disk of sun not visible

Rayleigh Scattering o Scattering by objects smaller than wavelengths of visible light

Primarily atmospheric gas molecules o Preferentially scatters shorter wavelengths

Violet and blue scattered most, red least. o Makes sky appear blue from earth and earth appear blue from space o Makes red sunsets

All wavelengths shorter than red scattered during long passage through atmosphere

Best sunsets occur with clear sky except for medium or high clouds

Reflect the red light against dark sky background

Mie Scattering Scattering by objects larger than wavelengths of visible light

(Water droplets and aerosols) o Scatters all wavelengths equally - white light o Makes clouds appear white o Aerosols near surface often make skies appear hazy white, especially

near horizon

Energy of light

Photons Light is wave, but is also considered as particle/corpuscle in quantum Mechanics and

the energy of an electromagnetic wave is considered to be quantized, i.e. it can only

exist in discrete amount. The basic unit of energy for an electromagnetic wave is called

a photon. The energy E of a photon is proportional to the wave frequency f,

E = h f

Where, the constant of proportionality h is the Planck's Constant,

h = 6.626 x 10-34 J s.

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Attenuation of solar radiation while passing thorough the atmosphere

Solar Constant=1.361 KW/m2

As the solar radiation passes through the atmosphere, the radiation is attenuated till

it reaches the surface of the earth. Then the incident radiation is by given by,

)exp()()( zkII topinc

Or )()()( topinc II , where )exp()( zk

Here, k is the coefficient of attenuation and )( is known as transmittance.

The transmittance of the atmosphere radiations is also given as as

)(

)()(

top

inc

I

I , i.e.

There are strong variation of atmospheric transmittance with . The figure 4 shows the atmospheric transmittance for the E-M spectrum.

Figure: The transmittance of E-M radiation through atmosphere (upper panel) and Blackbody radiation of sun and that from the earth’s surface (lower panel).

From the figure (upper panel) it appears that the solar radiations have high transmittance in the Near UV (λ=0.2-0.4 µm), visible (λ=0.4-0.7 µm) and Near Infrared (NIR) λ= (0.7-1.5 µm).

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Atmospheric window Parts of the electromagnetic spectrum that can be transmitted through the

atmosphere with relatively little interference. The atmosphere is considered as

window for the spectral radiation having high transmittance through the atmosphere

and such ranges of spectrum are termed as atmospheric windows. The figure shows

that the following rages of radiations are atmospheric window:

I. UV: 0.2-0.4 µm- (Solar Radiation Passive) II. Visible: 0.4-0.7 µm (Solar Radiation-Passive)

III. NIR: 0.7-1.5 µm (Solar Radiation-Passive) IV. Far Infrared (Thermal radiation): 8.0-15.0 µm (Blackbody radiation from earth’s

surface and lower atmosphere-Passive) V. Passive microwave (50 hz) Radiation from earth’s surface.

VI. Microwaves: centimeter to meter: Passive radiation from earth and Active radiation from Synthetic Aperture RADAR (SAR) that used direct Microwave beams for illuminating the object on earth.

Absorption Bands and Atmospheric Windows Some types of electromagnetic radiation easily pass through the atmosphere, while

other types do not. The ability of the atmosphere to allow radiation to pass through it

is referred to as its transmissivity, and varies with the wavelength/type of the

radiation. The gases that comprise our atmosphere absorb radiation in certain

wavelengths while allowing radiation with differing wavelengths to pass through.

The areas of the EM spectrum that are absorbed by atmospheric gases such as water

vapor, carbon dioxide, and ozone are known as absorption bands. In the figure,

absorption bands are represented by a low transmission value that is associated with

a specific range of wavelengths.

In contrast to the absorption bands, there are areas of the electromagnetic spectrum

where the atmosphere is transparent (little or no absorption of radiation) to specific

wavelengths. These wavelength bands are known as atmospheric "windows" since

they allow the radiation to easily pass through the atmosphere to Earth's surface.

Most remote sensing instruments on aircraft or space-based platforms operate in one

or more of these windows by making their measurements with detectors tuned to

specific frequencies (wavelengths) that pass through the atmosphere. When a remote

sensing instrument has a line-of-sight with an object that is reflecting sunlight or

emitting heat, the instrument collects and records the radiant energy.While most

remote sensing systems are designed to collect reflected radiation, some sensors,

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especially those on meteorological satellites, directly measure absorption

phenomena, such as those associated with carbon dioxide (CO2) and other gases. The

atmosphere is nearly opaque to EM radiation in part of the mid-IR and all of the far-IR

regions. In the microwave region, by contrast, most of this radiation moves through

unimpeded, so radar waves reach the surface (although weather radars are able to

detect clouds and precipitation because they are tuned to observe backscattered

radiation from liquid and ice particles).

The figure above shows the percentage of light transmitted at various wavelengths

from the near ultraviolet to the far infrared, and the sources of atmospheric opacity

are also given. You can see that there is plenty of atmospheric transmission of

radiation at 0.5 microns, 2.5 microns, and 3.5 microns, but in contrast there is a great

deal of atmospheric absorption at 2.0, 3.0, and about 7.0 microns. Both passive and

active remote sensing technologies do best if they operate within the atmospheric

windows.

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Microwave Remote Sensing

Electromagnetic radiation in the microwave wavelength region is used in remote

sensing to provide useful information about the Earth's atmosphere, land and ocean.

A microwave radiometer is a passive device which records the natural microwave emission from the earth. It can be used to measure the total water content of the atmosphere within its field of view.

A radar altimeter sends out pulses of microwave signals and record the signal scattered back from the earth surface. The height of the surface can be measured from the time delay of the return signals.

A wind scatterometer can be used to measure wind speed and direction over the ocean surface. It sends out pulses of microwaves along several directions and records the magnitude of the signals backscattered from the ocean surface. The magnitude of the backscattered signal is related to the ocean surface roughness, which in turns is dependent on the sea surface wind condition, and hence the wind speed and direction can be derived. orne platforms to generate high resolution images of the earth surface using microwave energy.

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Synthetic Aperture Radar (SAR) In synthetic aperture radar (SAR) imaging, microwave pulses are transmitted by an

antenna towards the earth surface. The microwave energy scattered back to the

spacecraft is measured. The SAR makes use of the radar principle to form an image by

utilising the time delay of the backscattered signals.

A radar pulse is transmitted from

the antenna to the ground

The radar pulse is scattered by the

ground targets back to the antenna.

In real aperture radar imaging, the ground resolution is limited by the size of the microwave beam sent out from the antenna. Finer details on the ground can be resolved by using a narrower beam. The beam width is inversely proportional to the size of the antenna, i.e. the longer the antenna, the narrower the beam.

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The microwave beam sent out by

the antenna illuminates an area

on the ground (known as the

antenna's "footprint"). In radar

imaging, the recorded signal

strength depends on the

microwave energy backscattered

from the ground targets inside

this footprint. Increasing the

length of the antenna will

decrease the width of the

footprint.

It is not feasible for a spacecraft to carry a very long antenna which is required for high resolution imaging of the earth surface. To overcome this limitation, SAR capitalises on the motion of the space craft to emulate a large antenna (about 4 km for the ERS SAR) from the small antenna (10 m on the ERS satellite) it actually carries on board.

Imaging geometry for a typical strip-mapping synthetic aperture radar imaging system.

The antenna's footprint sweeps out a strip parallel to the direction of the satellite's

ground track.

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Interaction between Microwaves and Earth's Surface When microwaves strike a surface, the proportion of energy scattered back to the

sensor depends on many factors:

Physical factors such as the dielectric constant of the surface materials which also depends strongly on the moisture content;

Geometric factors such as surface roughness, slopes, orientation of the objects relative to the radar beam direction;

The types of landcover (soil, vegetation or man-made objects). Microwave frequency, polarisation and incident angle.

All-Weather Imaging Due to the cloud penetrating property of microwave, SAR is able to acquire "cloud-

free" images in all weather. This is especially useful in the tropical regions which are

frequently under cloud covers throughout the year. Being an active remote sensing

device, it is also capable of night-time operation.

Basic mechanism of Remote Sensing and sensor design

Basic mechanism of remote sensing is that the outgoing radiation from the object

passes through the atmosphere up to the sensor onboard the remote sensing

platform. The platform is the aircraft or satellite or in special case balloons which carry

the sensor. The sensor has a field of view on which the resolution of the measurement

depends.

In designing the remote sensing sensors the electromagnetic waves / radiations are so

chosen that they belong to atmospheric window regions of the E-M spectrum. This

radiation may be solar radiation reflected from the object, or radiated by the object

due to thermal radiation or active beam generated by the Synthetic Aperture Radar

(SAR) and scattered by the object. This radiation scattered or emitted by the object is

sensed by the suitable sensors onboard the platforms. Then, the energy received by

the sensor is converted to digital signal or a physical value representing the properties

of the object on earth. The field of view determines how much area of the earth is

seen by the sensor providing single value of information. The sensor is able to scan the

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earth from left to right of the path of the satellite over a width equal to the field of

view. Thus strips of scanned information are received by the sensor as the satellite

moves. The path along which the satellite moves is called Nadir. The scanned

information over the strips (we call the strips as row) and individual observations in

the rows as column.

Figure: The Field of view, strips and viewed area / resolution

Figure: Illumination of the object and reflection form the object to the sensor; and

data recording and downloading to the ground station.

1. Energy Source or Illumination (A) – the first requirement for remote sensing is to have an energy source which illuminates or provides electromagnetic energy to the target of interest.

Strips or Rows

Field of view

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2. Radiation and the Atmosphere (B) – as the energy travels from its source to the target, it will come in contact with and interact with the atmosphere it passes through. This interaction may take place a second time as the energy travels from the target to the sensor. 3. Interaction with the Target (C) - once the energy makes its way to the target through the atmosphere, it interacts with the target depending on the properties of both the target and the radiation.

The following aspects are important for remote sensing:

Satellite or Aircraft

Sensors: Sensors uses reflected solar, earth’s infrared radiation and active beam

projected from the platform for illumination and scattered back to sensors

Multi spectral observation. This means the optical system on board the

platform looks at the object to capture radiation at different smaller spectral

bands. There are devices or filters which separates the chosen bands.

For SAR technology the multu-spectral, multi polarization and multi angle

remote sensing are made.

The information is gathered in known format in the storage system within the

platform and then beams down to the ground station

After receiving in the ground station, data are pre-processed, corrected for

geometrical error, geo-referenced in coordinate system

Then the process of image interpretation, image processing and classification

are done.

Applications are of manifold: land-use mapping, disaster monitoring, flood and

cyclone affected area mapping, ect. are performed through man-machine

interaction.

Remote Sensing Platforms The remote sensing of earth is materialized with sensors on board platforms are:

Aircrafts with special arrangements of making high resolution photographs on

photographic films using optical analog camera or digital camera or sensors.

The image quality depends on the atmospheric conditions. The images obtained

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through this imaging technique are known as aerial photograph. The

atmosphere should be cloud or fog free for getting high quality photographs.

The process of analysis and interpretation of aerial photographs is known as

photogrammetry.

Satellites are used as platforms with imaging instruments/sensors onboard

these satellites. The satellites are of 2-types depending on the orbit. The

satellites which are placed on the equator at a height of around 35000 km from

the earth surface have the same orbital time period as that of earth and is found

to be stationary with respect to the earth is known as Geostationary satellites.

Satellites having lower orbital heights of around 500-800 km are the known as

polar orbiting satellites. The orbital heights may be lower up to 300 km and is

known as Low Orbiting Satellites.

Balloons may also be used

Airborne Remote Sensing

In airborne remote sensing, downward or sideward looking sensors are mounted on an aircraft to obtain images of the earth's surface. An advantage of airborne remote sensing, compared to satellite remote sensing, is the capability of offering very high spatial resolution images (20 cm or less). The disadvantages are low coverage area and high cost per unit area of ground coverage. It is not cost-effective to map a large area using an airborne remote sensing system. Airborne remote sensing missions are often carried out as one-time operations, whereas earth observation satellites offer the possibility of continuous monitoring of the earth.

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Analog aerial photography, videography, and digital photography are commonly used in airborne remote sensing. Synthetic Aperture Radar imaging is also carried out on airborne platforms.

Analog photography is capable of providing high spatial resolution. The interpretation of analog aerial photographs is usually done visually by experienced analysts. The photographs may be digitized using a scanning device for computer-assisted analysis.

Digital photography permits real-time transmission of the remotely sensed data to a ground station for immediate analysis. The digital images can be analysed and interpreted with the aid of a computer.

A high resolution aerial photograph over a forested

area. The canopy of each individual tree can be

clearly seen. This type of very high resolution

imagery is useful in identification of tree types and

in assessing the conditions of the trees.

Another example of a high resolution aerial

photograph over a residential area.

Spaceborne Remote Sensing Eg. IKONOS 2; SPOT 1, 2, 4; EROS A1; OrbView 2 (SeaStar); NOAA 12, 14, 16; TERRA;

RADARSAT 1; ERS 1, 2.

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The receiving ground station at CRISP receives data from these satellites. In

spaceborne remote sensing, sensors are mounted on-board a spacecraft (space

shuttle or satellite) orbiting the earth. At present, there are several remote sensing

satellites providing imagery for research and operational applications. Spaceborne

remote sensing provides the following advantages:

Large area coverage; Frequent and repetitive coverage of an area of interest; Quantitative measurement of ground features using radiometrically

calibrated sensors; Semiautomated computerised processing and analysis; Relatively lower cost per unit area of coverage.

Satellite imagery has a generally lower resolution compared to aerial photography. However, very high resolution imagery (up to 1-m resolution) is now commercially available to civilian users with the successful launch of the IKONOS-2 satellite in September 24, 1999.

Airborne versus Spaceborne Radars Like other remote sensing systems, an imaging radar sensor may be carried on either an airborne or spaceborne platform. Depending on the use of the prospective imagery, there are trade-offs between the two types of platforms. Regardless of the platform used, a significant advantage of using a Synthetic Aperture Radar (SAR) is that the spatial resolution is independent of platform altitude. Thus, fine resolution can be achieved from both airborne and spaceborne platforms.

Although spatial resolution is independent of altitude, viewing geometry and swath coverage can be greatly affected by altitude variations. At aircraft operating altitudes, an airborne radar must image over a wide range of incidence angles, perhaps as much as 60 or 70 degrees, in order to achieve relatively wide swaths (let's say 50 to 70 km). As we have learned in the preceding sections, incidence angle (or look angle) has a significant effect on the backscatter from surface features and on their appearance on an image. Image characteristics such as foreshortening, layover, and shadowing will

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be subject to wide variations, across a large incidence angle range. Spaceborne radars are able to avoid some of these imaging geometry problems since they operate at altitudes up to one hundred times higher than airborne radars. At altitudes of several hundred kilometres, spaceborne radars can image comparable swath widths, but over a much narrower range of incidence angles, typically ranging from five to 15 degrees. This provides for more uniform illumination and reduces undesirable imaging variations across the swath due to viewing geometry.

Although airborne radar systems may be more susceptible to imaging geometry problems, they are flexible in their capability to collect data from different look angles and look directions. By optimizing the geometry for the particular terrain being imaged, or by acquiring imagery from more than one look direction, some of these effects may be reduced. Additionally, an airborne radar is able to collect data anywhere and at any time (as long as weather and flying conditions are acceptable!). A spaceborne radar does not have this degree of flexibility, as its viewing geometry and data acquisition schedule is controlled by the pattern of its orbit. However, satellite radars do have the advantage of being able to collect imagery more quickly over a larger area than an airborne radar, and provide consistent viewing geometry. The frequency of coverage may not be as often as that possible with an airborne platform, but depending on the orbit parameters, the viewing geometry flexibility, and the geographic area of interest, a spaceborne radar may have a revisit period as short as one day. As with any aircraft, an airborne radar will be susceptible to variations in velocity and other motions of the aircraft as well as to environmental (weather) conditions. In order to avoid image artifacts or geometric positioning errors due to random variations in the motion of the aircraft, the radar system must use sophisticated navigation/positioning equipment and advanced image processing to compensate for these variations. Generally, this will be able to correct for all but the most severe variations in motion, such as significant air turbulence. Spaceborne radars are not affected by motion of this type. Indeed, the geometry of their orbits is usually very stable and their positions can be accurately calculated. However, geometric correction

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of imagery from spaceborne platforms must take into account other factors, such as the rotation and curvature of the Earth, to achieve proper geometric positioning of features on the surface.

Satellite Orbits A satellite follows a generally elliptical orbit around the earth. The time taken to

complete one revolution of the orbit is called the orbital period. The satellite traces

out a path on the earth surface, called its ground track, as it moves across the sky. As

the earth below is rotating, the satellite traces out a different path on the ground in

each subsequent cycle. Remote sensing satellites are often launched into special

orbits such that the satellite repeats its path after a fixed time interval. This time

interval is called the repeat cycle of the satellite.

There are two different orbits for Satellites depending on the height of the satellites:

Polar orbiting satellites

The polar orbiting satellites are put at a height of around 300-1000 km from the surface of the earth have fixed orbit. The satellites move from south to north or north to south. The satellite covers the full earth as the earth rotates from west to east. The satellite passes from south to north and after the rotation by 180 degrees the satellite is found to pass from north to south. If the satellite passes from south to north it is known as ascending pass, while it passes from north to south it is known as descending pass. The satellite with orbital height of 800-1000 km takes about 120 second for a complete rotation. The orbital time increases with the increase of the height. Thus the low orbiting satellites have high orbital speed and lower time period.

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If the satellite crosses the orbit at a particular time of the day, then it is known as sun synchronous.

Geostationary Satellites

The geostationary orbits are such that the satellite moves in the same direction as that of the earth with same angular speed, as a result, the satellite appears to remain stationary with respect to earth and views the same region of the earth at certain interval of time which is from 10-30 minutes. Thus, the satellites on geostationary orbits are used for environmental monitoring and also for telecommunication purposes.

A geostationary satellite is launched in such a way that it follows an orbit parallel to the equator and travels in the same direction as the earth's rotation with the same period of 24 hours. Thus, it appears stationary with respect to the earth surface. A satellite following a geostationary orbit always views the same area on the earth. Thus, it is possible to monitor continuously any location within the field of view of its sensor. A large area of the earth can also be covered by the satellite.

Geostationary Orbit: The satellite appears stationary with respect to the

Earth's surface.

Remote Sensing Satellites Several remote sensing satellites are currently available, providing imagery suitable

for various types of applications. Each of these satellite-sensor platform is

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characterised by the wavelength bands employed in image acquisition, spatial

resolution of the sensor, the coverage area and the temporal coverge, i.e. how

frequent a given location on the earth surface can be imaged by the imaging system.

In terms of the spatial resolution, the satellite imaging systems can be classified into:

Low resolution systems (approx. 1 km or more) Medium resolution systems (approx. 100 m to 1 km) High resolution systems (approx. 5 m to 100 m) Very high resolution systems (approx. 5 m or less)

In terms of the spectral regions used in data acquisition, the satellite imaging systems can be classified into:

Optical imaging systems (include visible, near infrared, and shortwave infrared systems)

Thermal imaging systems Synthetic aperture radar (SAR) imaging systems

Optical/thermal imaging systems can be classified according to the number of spectral bands used:

Monospectral or panchromatic (single wavelength band, "black-and-white", grey-scale image) systems

Multispectral (several spectral bands) systems Superspectral (tens of spectral bands) systems Hyperspectral (hundreds of spectral bands) systems

Synthetic aperture radar imaging systems can be classified according to the combination of frequency bands and polarization modes used in data acquisition, e.g.:

Single frequency (L-band, or C-band, or X-band) Multiple frequency (Combination of two or more frequency bands) Single polarization (VV, or HH, or HV) Multiple polarization (Combination of two or more polarization modes)

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Types of Satellites and Sensors

Optical/Infrared Remote Sensing Satellites

Low Resolution

Geostationary Satellites Polar Orbiting Meteorological Satellites

o NOAA-AVHRR o DMSP-OLS

Orbview2-SeaWiFS SPOT4-Vegetation ADEOS-OCTS

Medium Resolution

TERRA-MODIS ENVISAT-MERIS ADEOS2-GLI

High Resolution

LANDSAT SPOT1,2,4 MOS EO1 IRS RESURS

Very High Resolution

IKONOS2 EROS-A1 Quickbird2 Orbview3 SPOT5

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Ground Truthing

[Accuracy] The accuracy of remotely sensed or mathematically calculated data based

on data actually measured in the field.

In order to "anchor" the satellite measurements, we need to compare them to something we know. One way to do this is by what we call "ground truth", which is one part of the calibration process. This is where a person on the ground (or sometimes in an airplane) makes a measurement of the same thing the satellite is trying to measure, at the same time the satellite is measuring it. The two answers are then compared to help evaluate how well the satellite instrument is performing. Usually we believe the ground truth more than the satellite, because we have more experience making measurements on the ground and sometimes we can see what we are measuring with the naked eye.

There are a number of ways to take ground truth measurements. The first is what we call a "field campaign". This is where several scientists and technicians take lots of equipment and set it up somewhere for a short but intense period of measurement. Often they go someplace rather simple, like the middle of the Great Plains in the United States, or an island or ship in the middle of the ocean, or an ice shelf at one of the poles. We get a lot of information from field campaigns, but they are expensive and only run for a short time.

Another source of ground truth is the on-going work of the National Weather Service. They have a record of weather conditions stretching back for over 100 years. Observations are made at regular intervals at offices around the country. These provide a nice record but are not necessarily taken at the same time a satellite passes over the spot. As clouds are very changeable, things can change completely in even a few minutes.

Another option for ground truth is S'COOL. Students at schools around the world can be involved by making an observation within a few minutes of the time that a satellite views their area.

Image Classification The intent of the classification process is to categorize all pixels in a digital image into

one of several land cover classes, or "themes". This categorized data may then be used

to produce thematic maps of the land cover present in an image. Normally,

multispectral data are used to perform the classification and, indeed, the spectral

pattern present within the data for each pixel is used as the numerical basis for

আরিফুল ইসলাম 28

categorization (Lillesand and Kiefer, 1994). The objective of image classification is to

identify and portray, as a unique gray level (or color), the features occurring in an

image in terms of the object or type of land cover these features actually represent on

the ground. Image classification is perhaps the most important part of digital image

analysis. It is very nice to have a "pretty picture" or an image, showing a magnitude of

colors illustrating various features of the underlying terrain, but it is quite useless

unless to know what the colors mean.

Digital image classification techniques group pixels to represent land cover features.

Land cover could be forested, urban, agricultural and other types of features. There

are three main image classification techniques.

Image Classification Techniques in Remote Sensing:

Unsupervised image classification

Supervised image classification

Object-based image analysis

Pixels are the smallest unit represented in an image. Image classification uses the

reflectance statistics for individual pixels. Unsupervised and supervised image

classification techniques are the two most common approaches. However, object-

based classification has been breaking more ground as of late.

What is the difference between supervised and unsupervised

classification?

Unsupervised Classification

Fig: Unsupervised Classification Example

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Pixels are grouped based on the reflectance properties of pixels. These groupings are

called “clusters”. The user identifies the number of clusters to generate and which

bands to use. With this information, the image classification software generates

clusters. There are different image clustering algorithms such as K-means and

ISODATA.

The user manually identifies each cluster with land cover classes. It’s often the case

that multiple clusters represent a single land cover class. The user merges clusters into

a land cover type. The unsupervised classification image classification technique is

commonly used when no sample sites exist.

Unsupervised Classification Steps:

Generate clusters

Assign classes

Fig: Unsupervised Classification Diagram

Supervised Classification

Fig: Supervised Classification Example: IKONOS

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The user selects representative samples for each land cover class in the digital image.

These sample land cover classes are called “training sites”. The image classification

software uses the training sites to identify the land cover classes in the entire image.

The classification of land cover is based on the spectral signature defined in the

training set. The digital image classification software determines each class on what it

resembles most in the training set. The common supervised classification algorithms

are maximum likelihood and minimum-distance classification.

Supervised Classification Steps:

Select training areas

Generate signature file

Classify

Fig: Supervised Classification Diagram

Object-Based (or Object-Oriented) Image Analysis Classification

Fig: Object-based Classification

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Traditional pixel-based processing generates square classified pixels. Object-based image classification is very different in that it generates objects of different shape and scale. This process is calledmulti-resolution segmentation.

Multiresolution segmentation produces homogenous image objects by grouping pixels. Objects are generated with different scales in an image simultaneously. These objects are more meaningful than the tradional pixel-based segmentation because they can be classified based on texture, context and geometry.

Object-based image analysis supports the use of multiple bands for multiresolution segmentation and classification. For example, infrared, elevation or existing shape files can simultaneously be used to classify image objects. Multiple layers can have context with each other. This context comes in the form of neighborhood relationships, proximity and distance between layers.

Nearest neighbor (NN) classification is similar to supervised classification. After multi-resolution segmentation, the user identifies sample sites for each land cover class. The statistics to classify image objects are defined. The object based image analysis software then classifies objects based on their resemblance to the training sites and the statistics defined.

Fig: Object-Based Classification Diagram

Object-Based Nearest Neighbor Classification Steps:

Perform multiresolution segmentation

Select training areas

Define statistics

Classify

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আরিফুল ইসলাম 33

Pixels A digital image comprises of a two dimensional array of individual picture elements called pixels arranged in columns and rows. Each pixel represents an area on the Earth's surface. A pixel has an intensity value and a location address in the two dimensional image.

The intensity value represents the measured physical quantity such as the solar radiance in a given wavelength band reflected from the ground, emitted infrared radiation or backscattered radar intensity. This value is normally the average value for the whole ground area covered by the pixel.

The intensity of a pixel is recorded as a digital number. Due to the finite storage capacity, a digital number is stored with a finite number of bits (binary digits). The number of bits determine the radiometric resolution of the image. For example, an 8-bit digital number ranges from 0 to 255 (i.e. 28 - 1), while a 11-bit digital number ranges from 0 to 2047. The detected intensity value needs to be scaled and quantized to fit within this range of value. In a Radiometrically Calibrated image, the actual intensity value can be derived from the pixel’s digital number.

The address of a pixel is denoted by its row and column coordinates in the two-dimensional image. There is a one-to-one correspondence between the column-row address of a pixel and the geographical coordinates (e.g. Longitude, latitude) of the imaged location. In order to be useful, the exact geographical location of each pixel on the ground must be derivable from its row and column indices, given the imaging geometry and the satellite orbit parameters.

Digital number Digital number in remote sensing systems, a variable assigned to a pixel, usually in the

form of a binary integer in the range of 0–255 (i.e. a byte). The range of energies

examined in a remote sensing system is broken into 256bins. A single pixel may have

several digital number variables corresponding to different bands recorded.

Digital Image A digital image is a two-dimensional array of pixels. Each pixel has an intensity value (represented by a digital number) and a location address (referenced by its row and column numbers).

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Analog Images Data collected in hard-copy pictorial records are termed photographs. Photographs

are recorded two-dimensionally on photosensitive emulsions that are either reflective

(such as in a print) or transmissive (such as a slide). In remote sensing, the term

photograph is reserved exclusively for images that were detected as well as recorded

on film. The more generic term image is used for any pictorial representation of image

data. Because the term image relates to any pictorial product, all photographs are

images, but not all images are photographs.

Image noise

Image noise is random (not present in the object imaged) variation of brightness or

color information in images, and is usually an aspect of electronic noise. It can be

produced by the sensor and circuitry of a scanner or digital camera. Image noise can

also originate in film grain and in the unavoidable shot noise of an ideal photon

detector. Image noise is an undesirable by-product of image capture that adds

spurious and extraneous information.

The original meaning of "noise" was and remains "unwanted signal"; unwanted

electrical fluctuations in signals received by AM radios caused audible acoustic noise

("static"). By analogy unwanted electrical fluctuations themselves came to be known

as "noise". Image noise is, of course, inaudible.

The magnitude of image noise can range from almost imperceptible specks on a digital

photograph taken in good light, to optical and radio astronomical images that are

almost entirely noise, from which a small amount of information can be derived by

sophisticated processing (a noise level that would be totally unacceptable in a

photograph since it would be impossible to determine even what the subject was).

Multilayer Image Several types of measurement may be made from the ground area covered by a single

pixel. Each type of measurement forms an image which carry some specific

information about the area. By "stacking" these images from the same area together,

a multilayer image is formed. Each component image is a layer in the multilayer

image.

Multilayer images can also be formed by combining images obtained from different sensors, and other subsidiary data. For example, a multilayer image may consist of

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three layers from a SPOT multispectral image, a layer of ERS synthetic aperture radar image, and perhaps a layer consisting of the digital elevation map of the area being studied.

fig: An illustration of a multilayer image consisting of five component layers.

Multispectral Image A multispectral image consists of a few image layers, each layer represents an image

acquired at a particular wavelength band. For example, the SPOT HRVsensor

operating in the multispectral mode detects radiations in three wavelength bands:

the green (500 - 590 nm), red (610 - 680 nm) and near infrared (790 - 890 nm) bands.

A single SPOT multispectral scene consists of three intensity images in the three

wavelength bands. In this case, each pixel of the scene has three intensity values

corresponding to the three bands.

A multispectral IKONOS image consists of four bands: Blue, Green, Red and Near Infrared, while a landsat TM multispectral image consists of seven bands: blue, green, red, near-IR bands, two SWIR bands, and a thermal IR band.

Superspectral Image The more recent satellite sensors are capable of acquiring images at many more wavelength bands. For example, the MODIS sensor on-board the NASA's TERRA satellite consists of 36 spectral bands, covering the wavelength regions ranging from the visible, near infrared, short-wave infrared to the thermal infrared. The bands have narrower bandwidths, enabling the finer spectral characteristics of the targets to be captured by the sensor. The term "superspectral" has been coined to describe such sensors.

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Hyperspectral Image

A hyperspectral image consists of about a hundred or more contiguous spectral bands. The characteristic spectrum of the target pixel is acquired in a hyperspectral image. The precise spectral information contained in a hyperspectral image enables better characterisation and identification of targets. Hyperspectral images have potential applications in such fields as precision agriculture (e.g. monitoring the types, health, moisture status and maturity of crops), coastal management (e.g. monitoring of phytoplanktons, pollution, bathymetry changes). Currently, hyperspectral imagery is not commercially available from satellites. There are experimental satellite-sensors that acquire hyperspectral imagery for scientific investigation (e.g. NASA's Hyperion sensor on-board the EO1 satellite, CHRIS sensor onboard ESA's PRABO satellite).

Panchromatic image A single band image generally displayed as shades of gray. Panchromatic emulsion is

a type of black-and-white photographic emulsion that is sensitive to

all wavelengths of visible light. Panchromatic film is a normal black-and-white film

which you can buy in the store and is what your grandparents probably used. It is called

panchromatic because it is sensitive to light of all visible colors (electromagnetic

wavelengths approximately 0.4mm to 0.7mm). Objects of different colors are

distinguishable in panchromatic film, with grayscale tones closely approximating the

brightness of the photograph's subject. Panchromatic film is typically not as sensitive

to subtle variation in green light as some other colors, making it difficult to

differentiate between similar vegetation types. To reduce atmospheric haze, a yellow

filter is normally used. For these images the filters believed to have been used were a

K2 (Wratten No. 8) and #12 deep amber (minus blue member of the cyan-magenta-

yellow set of subtractive primaries).

NDVI (Normalized Difference Vegetation Index)

The normalized difference vegetation index (NDVI) is a simple graphical indicator that

can be used to analyze remote sensing measurements, typically but not necessarily

from a space platform, and assess whether the target being observed contains live

green vegetation or not.

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Remote sensing phenology studies use data gathered by satellite sensors that measure

wavelengths of light absorbed and reflected by green plants. Certain pigments in plant

leaves strongly absorb wavelengths of visible (red) light. The leaves themselves

strongly reflect wavelengths of near-infrared light, which is invisible to human eyes.

As a plant canopy changes from early spring growth to late-season maturity and

senescence, these reflectance properties also change.

Many sensors carried aboard satellites measure red and near-infrared light waves

reflected by land surfaces. Using mathematical formulas (algorithms), scientists

transform raw satellite data about these light waves into vegetation indices. A

vegetation index is an indicator that describes the greenness — the relative density

and health of vegetation — for each picture element, or pixel, in a satellite image.

Although there are several vegetation indices, one of the most widely used is the

Normalized Difference Vegetation Index (NDVI). NDVI values range from +1.0 to -1.0.

Areas of barren rock, sand, or snow usually show very low NDVI values (for example,

0.1 or less). Sparse vegetation such as shrubs and grasslands or senescing crops may

result in moderate NDVI values (approximately 0.2 to 0.5). High NDVI values

(approximately 0.6 to 0.9) correspond to dense vegetation such as that found in

temperate and tropical forests or crops at their peak growth stage.

By transforming raw satellite data into NDVI values, researchers can create images and

other products that give a rough measure of vegetation type, amount, and condition

on land surfaces around the world. NDVI is especially useful for continental- to global-

scale vegetation monitoring because it can compensate for changing illumination

conditions, surface slope, and viewing angle. That said, NDVI does tend to saturate

over dense vegetation and is sensitive to underlying soil color.

NDVI values can be averaged over time to establish "normal" growing conditions in a

region for a given time of year. Further analysis can then characterize the health of

vegetation in that place relative to the norm. When analyzed through time, NDVI can

reveal where vegetation is thriving and where it is under stress, as well as changes in

vegetation due to human activities such as deforestation, natural disturbances such as

wild fires, or changes in plants' phenological stage.

UN-SPIDER In its resolution 61/110 of 14 December 2006 the United Nations General Assembly

agreed to establish the "United Nations Platform for Space-based Information for

Disaster Management and Emergency Response - UN-SPIDER" as a new United

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Nations programme, with the following mission statement: "Ensure that all countries

and international and regional organizations have access to and develop the capacity

to use all types of space-based information to support the full disaster management

cycle".

Whereas there have been a number of initiatives in recent years that have contributed

to making space technologies available for humanitarian and emergency response,

UN-SPIDER is the first to focus on the need to ensure access to and use of such

solutions during all phases of the disaster management cycle, including the risk

reduction phase, which will significantly contribute to reducing the loss of lives and

property.

The UN-SPIDER programme is achieving this by being a gateway to space information

for disaster management support, by serving as a bridge to connect the disaster

management and space communities and by being a facilitator of capacity-building

and institutional strengthening, in particular for developing countries.

UN-SPIDER is being implemented as an open network of providers of space-based

solutions to support disaster management activities. Besides Vienna (where UNOOSA

is located), the programme also has offices in Bonn, Germany and in Beijing, China.

Additionally, a network of Regional Support Offices multiplies the work of UN-SPIDER

in the respective regions.

Field of view The field of view (also field of vision, abbreviated FOV) is the extent of the observable world that is seen at any given moment. In case of optical instruments or sensors it is a solid angle through which a detector is sensitive to electromagnetic radiation.

IFOV (INSTANTANEOUS FIELD OF VIEW) A measure of the spatial resolution of a remote sensing imaging system. Defined as the angle subtended by a single detector element on the axis of the optical system. IFOV has the following attributes:

আরিফুল ইসলাম 39

Solid angle through which a detector is sensitive to radiation. The IFOV and the distance from the target determines the spatial resolution. A

low altitude imaging instrument will have a higher spatial resolution than a higher altitude instrument with the same IFOV.

Swath width Swath width refers to the strip of the Earth’s surface from which geographic data are collected by a moving vehicle such as asatellite, aircraft or ship in the course of swath mapping. The longitudinal extent of the swath is defined by the motion of the vehicle with respect to the surface, whereas the swath width is measured perpendicularly to the longitudinal extent of the swath. Note that swath width is also cited in the context of sonar specifications. It is measured and represented in metres or kilometres.

Fig: swath

Nadir

The nadir is the direction pointing directly below a particular location; that is, it is one of two vertical directions at a specified location, orthogonal to a horizontal flat surface there. Since the concept of being below is itself somewhat vague, scientists define the nadir in more rigorous terms. Specifically, in astronomy, geophysics and related sciences (e.g., meteorology), the nadir at a given point is the local vertical direction pointing in the direction of the force of gravity at that location. The direction opposite of the nadir is the zenith.

Nadir also refers to the downward-facing viewing geometry of an orbiting satellite, such as is employed during remote sensing of the atmosphere, as well as when an astronaut faces the Earth while performing a spacewalk.

আরিফুল ইসলাম 40

The word is also used figuratively to mean the lowest point of a person's spirits, or the quality of an activity or profession.

Fig: This diagram depicts a satellite observing backscattered sunlight in the nadir viewing geometry.

The term nadir can also be used to represent the lowest point reached by a celestial body during its apparent orbit around a given point of observation. This can be used to describe the location of the Sun, but it is only technically accurate for one latitude at a time and only possible at the low latitudes. The sun is said to be at the nadir at a location when it is at the zenith at the location's antipode.

In oncology, the term nadir is used to represent the lowest level of a blood cell count while a patient is undergoing chemotherapy. A diagnosis of neutropenic nadir after chemotherapy typically lasts 7–10 days.

Fig: Diagram showing the relationship between the zenith, the nadir, and different types of horizon.

The nadir is opposite the zenith.

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Image Processing and Analysis

Many image processing and analysis techniques have been developed to aid the interpretation of remote sensing images and to extract as much information as possible from the images. The choice of specific techniques or algorithms to use depends on the goals of each individual project. In this section, we will examine some procedures commonly used in analysing/interpreting remote sensing images.

Pre-Processing Prior to data analysis, initial processing on the raw data is usually carried out to correct

for any distortion due to the characteristics of the imaging system and imaging

conditions. Depending on the user's requirement, some standard correction

procedures may be carried out by the ground station operators before the data is

delivered to the end-user. These procedures include radiometric correction to correct

for uneven sensor response over the whole image and geometric correctionto correct

for geometric distortion due to Earth's rotation and other imaging conditions (such as

oblique viewing). The image may also be transformed to conform to a specific map

projection system. Furthermore, if accurate geographical location of an area on the

image needs to be known, ground control points (GCP's) are used to register the image

to a precise map (geo-referencing).

Image Enhancement

Enhancements are used to make it easier for visual interpretation and understanding of imagery. The advantage of digital imagery is that it allows us to manipulate the digital pixel values in an image. Although radiometric corrections for illumination, atmospheric influences, and sensor characteristics may be done prior to distribution of data to the user, the image may still not be optimized for visual interpretation. Remote sensing devices, particularly those operated from satellite platforms, must be designed to cope with levels of target/background energy which are typical of all conditions likely to be encountered in routine use. With large variations in spectral response from a diverse range of targets (e.g. forest, deserts, snowfields, water, etc.) no generic radiometric correction could optimally account for and display the optimum brightness range and contrast for all targets. Thus, for each application and each image, a custom adjustment of the range and distribution of brightness values is usually necessary.

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Multispectral SPOT image of the same area shown in a previous section, but acquired

at a later date. Radiometric and geometric corrections have been done. The image

has also been transformed to conform to a certain map projection (UTM projection).

This image is displayed without any further enhancement.

In the above unenhanced image, a bluish tint can be seen all-over the image, producing a hazy apapearance. This hazy appearance is due to scattering of sunlight by atmosphere into the field of view of the sensor. This effect also degrades the contrast between different landcovers.

It is useful to examine the image Histograms before performing any image enhancement. The x-axis of the histogram is the range of the available digital numbers, i.e. 0 to 255. The y-axis is the number of pixels in the image having a given digital number. The histograms of the three bands of this image is shown in the following figures.

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Histogram of the XS3 (near infrared) band (displayed in red). Histogram of the XS2 (red) band (displayed in green). Histogram of the XS1 (green) band (displayed in blue). Note that the minimum digital number for each band is not zero. Each histogram is shifted to the right by a certain amount. This shift is due to the atmospheric scattering component adding to the actual radiation reflected from the ground. The shift is particular large for the XS1 band compared to the other two bands due to the higher contribution from Rayleigh scattering for the shorter wavelength.

The maximum digital number of each band is also not 255. The sensor's gain factor has been adjusted to anticipate any possibility of encountering a very bright object. Hence, most of the pixels in the image have digital numbers well below the maximum value of 255.

The image can be enhanced by a simple linear grey-level stretching. In this method, a level threshold value is chosen so that all pixel values below this threshold are mapped to zero. An upper threshold value is also chosen so that all pixel values above this threshold are mapped to 255. All other pixel values are linearly interpolated to lie between 0 and 255. The lower and upper thresholds are usually chosen to be values close to the minimum and maximum pixel values of the image. The Grey-Level Transformation Table is shown in the following graph.

Grey-Level Transformation Table for performing linear grey level stretching

of the three bands of the image. Red line: XS3 band; Green line: XS2 band;

Blue line: XS1 band.

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The result of applying the linear stretch is shown in the following image. Note that

the hazy appearance has generally been removed, except for some parts near to the

top of the image. The contrast between different features has been improved.

Multispectral SPOT image after enhancement by a simple linear greylevel stretching.

Passive Remote Sensing Remote sensing systems which measure energy that is naturally available are called passive sensors. Passive sensors can only be used to detect energy when the naturally occurring energy is available

Every day the Earth’s surface is hit by incoming electromagnetic (EM) radiation from the sun. This EM radiation is known as incident energy (Ei).

There are three fundamental energy interactions with incident energy: 1) reflected energy (Er) 2) absorbed energy (Ea) 3) transmitted energy (Et)

Incident Energy Formula: Ei = Er + Ea + Et

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Active Remote Sensing The sensor emits radiation which is directed toward the target to be investigated. The radiation reflected from that target is detected and measured by the sensor. Active remote sensing is being done any time of the day. Active remote Sensors create their own electromagnetic energy that • is transmitted from the sensor toward the terrain • interacts with the terrain producing a backscatter of energy • is recorded by the remote sensor’s receiver. Active remote sensing, that is, using radiation-emitting devices or substances to produce electromagnetic waves that ‘artificially’ illuminate the Earth's surface. As in the act of ‘seeing’, for example: One would turn on the light when it's too dark to see.

Active Passive

Using artificial energy source Using natural energy source

Done any time of the day Work only the day when sun is present

used for examining wavelengths that’s your need

Sensors can measure visible, infrared, ultraviolet and more types of EM radiation of sun ray that’s reflected form the object.

High cost Low cost

LIDAR image Landsat image

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Image resolution

Digital images are made up of thousands of pixels (blocks of colour), and the number of pixels in the image will determine how high the image's resolution is.

The image's resolution is the number of pixels divided by the size it is being viewed at - so if the image is 720 pixels wide by 720 pixels tall, and it's being viewed at 10 inches square, it has a resolution of 72 pixels per inch (I'm not sure why it's measured in inches - but it's probably a combination of the fact it originated in print before metrication and that computers were so heavily based in the US).

Resolution refers to the number of pixels in an image. Resolution is sometimes identified by the width and height of the image as well as the total number of pixels in the image. For example, an image that is 2048 pixels wide and 1536 pixels high (2048X1536) contains (multiply) 3,145,728 pixels (or 3.1 Megapixels).

Advantage of high resolution image

Sub-meter (VHR) resolution satellites provides users a smaller ground sampling distance (GSD) than other imaging satellites, making them more suitable for reliable Earth observation, site monitoring, object identification and many other tasks requiring precision data.

Whether natural or man-made, objects and landscapes may be examined and interpreted using very high spatial resolution satellite imagery. Among its many advantages, VHR images enable identification of specific models of vehicles, vessels and airplanes, as well as structural changes, consequences of natural disasters and monitoring of operations – whether of industrial facilities or farm livestock. This versatility makes EROS imagery a powerful resource for civilian and military needs.

Sub-half-meter imagery provides more accurate feature detection and identification,

allowing you to detect smaller features and identify them more accurately. This will

aid you in more accurate and automated land use/land cover mapping, and provide

improved change monitoring identifying smaller changes. Ex: Mapping, Natural

resource management, urban planning, Emergency response

Location-based Services

With enhanced resolution capabilities, companies in the Location Based Services (LBS)

industry will be able to provide their customers and portal users with crisper, clearer

base map images, allowing for better recognition of features.

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Example Applications:

> Automobile and personal navigation mapping

> Fleet management

> Nautical navigation

> Online portals

Energy

A wide range of energy-specific industries will be able to take advantage of our higher

resolution capabilities. From more accurate monitoring of pipeline assets or

construction areas to higher levels of detail and accuracy in volumetric calculations,

our enhanced imagery completeness will help you do your job more efficiently and

effectively.

Example Applications:

> Oil & Gas facility and resource monitoring

> Drilling and resource exploration

> Emergency management and response

> Industry production information gathering

Disadvantage of Low resolution image

Real-time data Real-time data denotes information that is delivered immediately after collection.

There is no delay in the timeliness of the information provided. Real-time data is often

used for navigation or tracking. Some uses of this term confuse it with the term

dynamic data.

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Solid angle

In geometry, a solid angle (symbol: Ω) is the two-dimensional angle in three-dimensional space that an object subtends at a point. It is a measure of how large the object appears to an observer looking from that point. In the International System of Units (SI), a solid angle is expressed in a dimensionless unit called a steradian (symbol: sr).

A small object nearby may subtend the same solid angle as a larger object farther away. For example, although the Moon is much smaller than the Sun, it is also much closer to Earth. Indeed, as viewed from any point on Earth, both objects have approximately the same solid angle as well as apparent size. This is evident during a solar eclipse

Look Angle

The elevation and azimuth at which a particular satellite is predicted to be found at a specified time. look angle In radar terminology, the angle between the vertical plane passing through the radar antenna and the line between the antenna and object.

Look angles are required such that the earth station antenna points or “looks at” the satellite directly. From a location on earth, the 2 look angles that are needed are Azimuth and Elevation. Since XTAR satellites are geostationary, these look angles are constant for fixed ground antennae.

The look angle calculator determines the azimuth and elevation angles for XTAR satellites based on the input earth station latitude (°N) and longitude (°E) geographical locations.

Look Angle Calculator

To use for XTAR satellites, make the following selections and entries:

Select either: o 29E XTAR-EUR o 30W HISPASAT 1C (aka XTAR-LANT

aka SpainSat)

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Select your location by either interactively clicking on the map or by entering your address into the dropdown. Alternatively, your latitude (°N) and longitude (°E) can be entered into this dropdown in decimal degrees (separated with comma but do not use parenthesis)

The resulting Azimuth and elevation look angles appear interactively at the base of map.

The magnetic Azimuth is also given for compass reference.

The LNB skew is not used for XTAR since our satellites use circular polarization.

The Options box for “show obstacle” can be checked to display the maximum height, h and minimum distance, d that an obstacle can have without blocking the line of sight from your location to the satellite.

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Spectral Signature In Remote Sensing

Features on the Earth reflect, absorb, transmit, and emit electromagnetic energy from the

sun. Special digital sensors have been developed to measure all types of electromagnetic

energy as it interacts with objects in all of the ways listed above. The ability of sensors to

measure these interactions allows us to use remote sensing to measure features and changes

on the Earth and in our atmosphere. A measurement of energy commonly used in remote

sensing of the Earth is reflected energy (e.g., visible light, near-infrared, etc.) coming from

land and water surfaces. The amount of energy reflected from these surfaces is usually

expressed as a percentage of the amount of energy striking the objects. Reflectance is 100%

if all of the light striking and object bounces off and is detected by the sensor. If none of the

light returns from the surface, reflectance is said to be 0%. In most cases, the reflectance

value of each object for each area of the electromagnetic spectrum is somewhere between

these two extremes. Across any range of wavelengths, the percent reflectance values for

landscape features such as water, sand, roads, forests, etc. can be plotted and compared.

Such plots are called “spectral response curves” or “spectral signatures.” Differences among

spectral signatures are used to help classify remotely sensed images into classes of landscape

features since the spectral signatures of like features have similar shapes. The figure below

shows differences in the spectral response curves for healthy versus stressed sugar beet

plants.

The more detailed the spectral information recorded by a sensor, the more information that

can be extracted from the spectral signatures. Hyperspectral sensors have much more

detailed signatures than multispectral sensors and thus provide the ability to detect more

subtle differences in aquatic and terrestrial features.