Modern Remote Sensing: Imagery, Capabilities, Possibilities Paul F. Hopkins [email protected] [email protected] 315.470.6696 Workshop on Advanced Technologies

Modern Remote Sensing: Imagery,

Capabilities, Possibilities

Paul F. [email protected]

315.470.6696

Workshop on Advanced Technologies in Real-Time Monitoring and Modeling for Drinking Water Safety and

Security

mailto:[email protected]

Remote Sensing

Water Safety and Security Workshop Paul F. Hopkins

Presentation Content• Basic principles of remote sensing• Traditional and modern imagery resources• Digital image processing

Preprocessing Information extractionAccuracy assessment

• Additional topics in image processingModern approaches to information extractionChange detectionData fusion

• A few examples

Remote Sensing


What is Remote Sensing?

Remotedistant or withoutphysical contact

Perceiving or studying interesting properties andobjects without physically contacting them

Senseperceive, feel, or studyproperties or objects

Remote Sensing


Remote Sensing Process• Three general stages

Acquiring image dataProcessing image data to produce informationCommunicating and using information

• Expectations must be reasonable Information providers and users need to be

knowledgeable We understand the capabilities of the “traditional”

image data sources and applicationsMuch less understanding about recent remote

sensing technologies

Remote Sensing


Energy-Surface Interactions• When energy strikes a surface, three

interactions can occur:ReflectionAbsorptionTransmission

• Generally, in remote sensing, reflection is of most interestReflectanceDegree of reflectance varies with wavelengthFor visible energy, spectral reflectance produces

the colors we perceive

Remote Sensing





• A few examples

Remote Sensing


“Traditional” Remote Sensing• Aerial photography and expert processing

image interpretation (photointerpretation) Image measurement (photogrammetry)

• Satellite digital imagery of moderate resolution and computer processingWeather satellites, Landsat, SPOT, and IRS Image processing procedures

– Enhancements and transformations– Spectral pattern recognition

Data continuity

Remote Sensing


“Modern” Remote Sensing (data)• High spatial resolution digital imagery

On the order of 1m or better resolutionExceptional spatial detail but many new

challenges

• High spectral resolution imageryDozens, if not hundreds, of spectral bandsFundamental changes in processing data

• High temporal resolution imagery• Radar (microwave) digital imagery

Operational advantages Information content very different than others

• Lidar

Remote Sensing


Idea of Spatial Resolution

1 meter pixel 30 meter pixel

Remote Sensing


Comparison of Spectral Resolutions

MULTISPECTRALMULTISPECTRAL Number of Bands: TensNumber of Bands: TensBandwidth : WideBandwidth : Wide (micrometers ((micrometers (m))m))Spectral Resolution: MediumSpectral Resolution: Medium

HYPERSPECTRALHYPERSPECTRAL Number of Bands: HundredsNumber of Bands: HundredsBandwidth: NarrowBandwidth: Narrow (nanometers (nm))(nanometers (nm))(Narrower in reflective region (Narrower in reflective region than in emissive region)than in emissive region)Spectral Resolution: HighSpectral Resolution: High

ULTRASPECTRALULTRASPECTRAL Number of Bands: Thousands Number of Bands: Thousands Bandwidth: Bandwidth: Very Narrow Very Narrow (<1 nanometer)(<1 nanometer)Spectral Resolution: Very HighSpectral Resolution: Very High

Detects solids and liquidsDetects solids and liquids

Detects and identifies solids, Detects and identifies solids, liquids, and some gasesliquids, and some gases

Detects and identifies solids, Detects and identifies solids, liquids, and gasesliquids, and gases

400 nm 700 nm

Near Midwave Longwave InfraredInfrared

ShortwaveUltravioletInfrared Infrared

1100 nm 3000 nm

RGB

5000 nm

LANDSAT (TM) - 7 BANDS (MS)

HYDICE - 210 BANDS (HS)

SEBASS - 256 BANDS (HS)

AES - 26,000 BANDS (US)

14000 nm

Remote Sensing


Imaging Spectroscopy

P ix e l R e f le c ta n c e

A P ix e l S p e c tru mA P ix e l S p e c tru m

Ref

lect

ance

Ref

lect

ance

W a v e le n g thW a v e le n g th

R,G,B ValuesR,G,B Values

xx

x

A single pixel

A Pixel

Hyperspectral data cube

North

East Spectra

l

•Hyperspectral data provides considerable information about the surface materials

•Multispectral imagery provides only a few channels of information

Multispectral Reflectance

Remote Sensing


HYPERION

• Satellite system with spatial resolution: 30 m

• Spectral resolution: 220 bands (from 0.4 to 2.5 µm)

http://eo1.gsfc.nasa.gov/Technology/Hyperion.html








MODIS

• Spectral resolution: 36 discrete spectral bands Bands 1-19 in the range of 620 to 965 nanometers Bands 20-36 in the range of 3.6 to 14.3 micrometers

http://modis.gsfc.nasa.gov/

• Spatial Resolution: Varies from 250 m to 1000 m

• Temporal: Entire Earth every one to two days

• Suited for regional applications

Snow Cover to north Clouds to eastFebruary 28, 2002










Remote Sensing


ASTER

• Spectral resolution: 14 discrete spectral bands

http://asterweb.jpl.nasa.gov/

• Spatial: Varies band to band 15 m (bands 1-3 VNIR) 30 m (bands 4-9 SWIR) 90 m (bands 10-14

Thermal)

• VNIR band 3 has both forward and nadir looking components to produce stereo imagery

Onondaga LakeSyracuse, NYJune 19, 2000










Remote Sensing


Backscatter Coefficient Images

JERS-1 (April, 95) ERS-1 (August, 95)

Direct Geopositioning

Remote Sensing


LIDAR• LIght Detection And Ranging• The LIDAR instrument transmits light

out to a target • Some of this light is reflected and/or

scattered back to the instrument where it is analyzed

• The change in the properties of the light enables some properties of the target to be determined

• The time for the light to travel out to the target and back is used to determine the range to the target

• Direct Geopositioning is crucial• Digital Elevation Model or DEM is often produced

Remote Sensing


LIDAR image

Tully Valley NY

Remote Sensing



Preprocessing (restoration and enhancement) Information extraction (classification)Evaluation (accuracy assessment)


• A few examples

Remote Sensing


Radiometric Restoration(sensor problems)

Remote Sensing


Radiometric Restoration

Downwelling Absorption &

Scattering

Direct & Adjacent Reflection

Upwelling Absorption

& Scattering

Atmospheric Path

Radiance

Remote Sensing


Geometric Restoration

Remote Sensing


Contrast Enhancement

Remote Sensing


Ratios and Indices RedIR

RedIRNDVI

Spectral Transform (PCA)

1 2

3

Remote Sensing


Classification• Perform statistical pattern recognition

Assume spectral (or other) measurements have unique patterns for the classes of interest

Use computer routines to generate statistical descriptions of these patterns and classes

Relate image values to the statistical descriptions of classes

– Identify a strategy for deciding which class is most similar to the image location under consideration

– Apply the decision strategy to all image values and assign class identities

• Postprocess, if desired

Remote Sensing


Idea of Training

Remote Sensing


Classification Result

Remote Sensing


Accuracy Assessment• Uses idea of a contingency or confusion table

(also termed an “error matrix”)• Compare a sample of reference locations with

the class assigned by the classifier

Classified Category

Reference Category Total

A B

A 90 6 96

B 10 94 104

Total 100 100 200

Remote Sensing





• A few examples

Remote Sensing


“Modern” Remote Sensing (processing)

• Complementary technologies (GPS, GIS)• Analytical photogrammetry and methods for

geometrically processing imagery (DOQQs)• Information technology and computer

processing, generally and specifically for image processing Image/Spatial modeling & expert classifiersAdaptive computingChange detectionData fusion

Example Image Model

(to find trees in high

resolution imagery)

Remote Sensing


Input Image

Remote Sensing


Model Result

Remote Sensing


Example of Adaptive Computing (Genetic Algorithm Approach)

20 22 24 23 30 33 18

30 31 40 39 38 43 29

30 40 49 53 59 54 50

41 56 63 84 82 76 72

35 42 47 50 48 53 63

32 33 42 50 37 43 31

19 23 26 24 32 40 32

20 22 24 23 30 33 18 30 31 40 39 38 43 29 30 40 49 53 59 54 50 41 56 63...

First row Second row

Template of Tree Crown

Chromosome

Remote Sensing


Example Templates

Manually generated

GA-evolved

Remote Sensing


Genetic Algorithm Output

Manually generatedtemplate

GA evolvedtemplate

Remote Sensing


Genetic Algorithm Results

Classified Tree Not Tree

User’s Accuracy

Tree 41 16 72%

Not Tree 0 24 100%

Producer’s Accuracy

100% 60% Overall: 80%

Classified Tree Not Tree

User’s Accuracy

Tree 37 0 100%

Not Tree 4 40 90%

Producer’s Accuracy

90% 100% Overall: 95%

Manually generated template

GA evolved template

Remote Sensing


Change Detection• Numerous methods and the best method will

depend on the type and amount of change• Accurate registration is critical• Some methods require accurate

normalization to remove variations that are not caused by land changesAtmosphereEnergy source – target – sensor variations

• Errors in the input images will compound each other and produce greater errors in the change detection results

Remote Sensing


Image Fusion• Many different image types (resolutions)• Combining more than one type might provide

enhanced capabilitySharpening with higher spatial resolution dataPhenological exploitation with high temporal

resolution dataEnhanced spectral pattern distinctions with higher

spectral resolution data

• Selected methods Intensity – hue – saturation (ihs) transformsPrincipal component substitutionHigh pass frequency substitution

Remote Sensing


IHS Transform

Remote Sensing


Principal Component Substitution

Remote Sensing





• A few examples

Remote Sensing


Remote Sensing


Remote Sensing


Remote Sensing


Onondaga LakeASTER Image(19 June 2000)

Remote Sensing


Onondaga LakeEmerge Imagery(July 1999)

Remote Sensing


LIDAR Application

1997 1998

Remote Sensing


Documents

Modern Remote Sensing: Imagery, Capabilities, Possibilities Paul F. Hopkins [email protected] [email protected] 315.470.6696 Workshop on Advanced Technologies