Post on 21-Dec-2015
Modern Remote Sensing: Imagery,
Capabilities, Possibilities
Paul F. Hopkinsphopkins@syr.edu
315.470.6696
Workshop on Advanced Technologies in Real-Time Monitoring and Modeling for Drinking Water Safety and
Security
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
Water Safety and Security Workshop Paul F. Hopkins
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
Water Safety and Security Workshop Paul F. Hopkins
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
Water Safety and Security Workshop Paul F. Hopkins
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
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
Water Safety and Security Workshop Paul F. Hopkins
“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
Water Safety and Security Workshop Paul F. Hopkins
“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
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Idea of Spatial Resolution
1 meter pixel 30 meter pixel
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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
Water Safety and Security Workshop Paul F. Hopkins
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
Water Safety and Security Workshop Paul F. Hopkins
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
Water Safety and Security Workshop Paul F. Hopkins
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
Water Safety and Security Workshop Paul F. Hopkins
Backscatter Coefficient Images
JERS-1 (April, 95) ERS-1 (August, 95)
Direct Geopositioning
Remote Sensing
Water Safety and Security Workshop Paul F. Hopkins
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
Water Safety and Security Workshop Paul F. Hopkins
LIDAR image
Tully Valley NY
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 (restoration and enhancement) Information extraction (classification)Evaluation (accuracy assessment)
• Additional topics in image processingModern approaches to information extractionChange detectionData fusion
• A few examples
Remote Sensing
Water Safety and Security Workshop Paul F. Hopkins
Radiometric Restoration(sensor problems)
Remote Sensing
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Radiometric Restoration
Downwelling Absorption &
Scattering
Direct & Adjacent Reflection
Upwelling Absorption
& Scattering
Atmospheric Path
Radiance
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Geometric Restoration
Remote Sensing
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Contrast Enhancement
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Ratios and Indices RedIR
RedIRNDVI
Spectral Transform (PCA)
1 2
3
Remote Sensing
Water Safety and Security Workshop Paul F. Hopkins
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
Water Safety and Security Workshop Paul F. Hopkins
Idea of Training
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Water Safety and Security Workshop Paul F. Hopkins
Classification Result
Remote Sensing
Water Safety and Security Workshop Paul F. Hopkins
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
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
Water Safety and Security Workshop Paul F. Hopkins
“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)
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Water Safety and Security Workshop Paul F. Hopkins
Input Image
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Model Result
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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
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Example Templates
Manually generated
GA-evolved
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Genetic Algorithm Output
Manually generatedtemplate
GA evolvedtemplate
Remote Sensing
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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
Water Safety and Security Workshop Paul F. Hopkins
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
Water Safety and Security Workshop Paul F. Hopkins
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
Water Safety and Security Workshop Paul F. Hopkins
IHS Transform
Remote Sensing
Water Safety and Security Workshop Paul F. Hopkins
Principal Component Substitution
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
Water Safety and Security Workshop Paul F. Hopkins
Remote Sensing
Water Safety and Security Workshop Paul F. Hopkins
Remote Sensing
Water Safety and Security Workshop Paul F. Hopkins
Remote Sensing
Water Safety and Security Workshop Paul F. Hopkins
Onondaga LakeASTER Image(19 June 2000)
Remote Sensing
Water Safety and Security Workshop Paul F. Hopkins
Onondaga LakeEmerge Imagery(July 1999)
Remote Sensing
Water Safety and Security Workshop Paul F. Hopkins
LIDAR Application
1997 1998
Remote Sensing
Water Safety and Security Workshop Paul F. Hopkins