CHARACTERISTICS OF REMOTELY SENSED IMAGERY

Spatial Resolution

• There are a number of ways in which images can differ.

• One set of important differences relate to the various resolutions that images express.

• Resolution: the fineness of detail that can be distinguished in an image.

• Detail, however, can refer to several different dimensions.

RESOLUTION?

HOW CAN IMAGES DIFFER?

𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹 𝐹𝐹𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝐴𝐴𝐹𝐹𝑙𝑙𝐴𝐴𝑙𝑙𝐴𝐴𝐴𝐴𝑙𝑙

How is the scale of a photograph determined?

HOW HIGH CAN WE GO?

Ikonos 2

Both have sun-synchronous orbits of around 700 km.

𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹 𝐹𝐹𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝑙𝐴𝐴𝐹𝐹𝑙𝑙𝐴𝐴𝑙𝑙𝐴𝐴𝐴𝐴𝑙𝑙

How is the scale of a photograph determined?

HOW HIGH CAN WE GO?

Low Earth Orbit satellites

Altitude 681 km

MODIS:Ice and Clouds in the Bering Strait

MODIS: Yellow River Delta 2009

250 m spatial resolution @ 705 km

Low Earth Orbit satellites

AVHRR – 1 km resolution @ a nominal altitude of 833 km

Low Earth Orbit satellites

MIDDLE EARTH ORBITS

Also

GOES image, spatial resolution varies from 1 to 13+ km @ 35,800 km above the Earth

Geostationary satellites

SPATIAL RESOLUTION

• The fineness of detail visible in an image.– For photographic film, a function of the grain size and the camera lens

(line pairs/mm)– For sensors, a function of the sensor’s characteristics (IFOV) [Angle A]

• For any sensor– atmospheric conditions, – the viewing angle (perpendicular or oblique), – the height of the sensor’s platform, and – the stability of the platform

all affect the resolution.

• (View the science education interactive footprint demo.)

SPATIAL RESOLUTION

APPARENTLY COARSER OR FINER

Contrast enhancement:Something you’ve workedon in your labs.

Kutztown University in Pennsylvaniawhere we can discern details far smallerthan the 0.5m pixel size. GeoEye-1

• Other factors that influence the spatial resolution include:– Characteristics of the features being observed (high

contrast vs low contrast—e.g., lines in the GeoEye image)– Relation of the feature geometry to the sensor geometry

(see also mixed pixels)– How the data is displayed

• View the ‘what a sensor sees’ interactive demo.

SPATIAL RESOLUTION

Image processing can increase our ability to discern features

• There are several terms used in describing the spatial resolution of data:

– Ground Sampling Distance (GSD): for digital imagery, the ground distance represented by the width of a pixel [B]

– Ground Resolving Distance (GRD): for photographic film, the size of the smallest object expected to be detected

• Both are a function ofthe scale at which theimage is displayed.

SPATIAL RESOLUTION

• The true resolution of the sensor [Collection GSD] may differ from the pixel size [Product GSD] (data can be resampledand presented as being coarser or finer than it inherently is).

DATA DISPLAY

10 m resolution, 10 m pixel size

30 m resolution, 10 m pixel size

80 m resolution,10 m pixel size

DATA DISPLAY

4 meter resolution IKONOS imagewith 40 meter grid overlaid.

IKONOS image resampled to 40m

• If we assume that within each pixel there will be a unique set of Digital Numbers (DN) that we can match to a single spectral reflectance curve, mixed pixels are obviously a significant problem.

MIXED PIXELS

Higher spatial resolution can reduce the number of mixed pixels, but at a cost.

MIXED PIXELS

• With high spatial resolution comes aliasing, where a non-existent pattern appears because of sample spacing.

• With crops, spatial sampling on the scale of a meter combines with the row spacing to alias pseudo-rows that are tens of meters wide.

• The apparent rows are false. Visual clues to this are seen by comparing the apparent row spacing with the size of roads and homes. The imagery was taken with a nominal ground resolution of approximately 2 meters. The spacing of the rows is less.

SPATIAL RESOLUTION AND ALIASING

Combining higher spatial resolution data with higher spectral resolution data is called pan sharpening

PAN SHARPENINGQuickbird data (50cm panchromatic, 2m multispectral)

• There are several methods by which pan sharpened images are produced.

• One common method is HSI, which stands for "Hue Saturation Intensity" (ESRI’s description).

• The lower resolution RGB image is upsampled (i.e., resampled to match the higher spatial resolution pixel size) and converted to HSI space.

• The panchromatic band is then matched and substituted for the Intensity band.

• The HSI image is converted back to RGB space.

PAN SHARPENING

PAN SHARPENING

High-resolution panchromatic images (spatial resolution 0.6 m)

Low-resolution natural colour images: QuickBird(spatial resolution 2.4 m)

PICK YOUR SPATIAL RESOLUTION

The highest resolution data isn’t always necessary, or even the ‘best’ to use.

PICKING THE RIGHT SPATIAL RESOLUTION

CAN’T SEE THE FOREST FOR THE TREES

Basic elements Primary

Spatial arrangements of tone

Secondary

Tertiary

Quaternary

Tone Colour

Size Shape Texture

Pattern Height Shadow

Site Association

ELEMENTS OF IMAGE INTERPRETATION

Degreeof

complexity

Coarser-resolution imagery

Finer-resolution imagery

Identifying ‘forests’ and ‘urban areas’ vscollections of individual trees and houses, streets, lawns, etc.

SPATIAL / SPECTRAL RESOLUTIONS

• Fine/high spatial resolution > small IFOV– Small IFOV > reduction in energy detected > less

radiometric resolution• increase the amount of energy detected (radiometric

resolution) – without reducing spatial resolution

• broaden the wavelength range detected for a particular channel or band (decrease the spectral resolution)

– reduce spatial resolution • improved radiometric and/or spectral resolution.

SPATIAL / RADIOMETRIC RESOLUTION

MULTI-RESOLUTIONS FOR INTEGRATED STUDIES.

• Spatial resolution has steadily increased over time.• Higher resolution data comes with added costs:

– Size of files– More intelligent analysis of the data required– Smaller areas sampled (footprints of sensors)

• But also added benefits:– Many more application areas (esp. urban)

SUMMARY