remote sensing hyperspectral remote sensing

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Remote Sensing Remote Sensing Hyperspectral Remote Hyperspectral Remote Sensing Sensing

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Remote Sensing Hyperspectral Remote Sensing. 1. Hyperspectral Remote Sensing. Collects image data in many narrow contiguous spectral bands through the visible and infrared portions of spectrum The band width is < 10nm 1mm = 1,000 m m 1 m m = 1,000nm. - PowerPoint PPT Presentation

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Page 1: Remote Sensing Hyperspectral Remote Sensing

Remote SensingRemote Sensing

Hyperspectral Remote SensingHyperspectral Remote Sensing

Page 2: Remote Sensing Hyperspectral Remote Sensing

1. Hyperspectral Remote Sensing1. Hyperspectral Remote Sensing► Collects image data in many narrow Collects image data in many narrow

contiguous spectral bands through the contiguous spectral bands through the visible and infrared portions of spectrumvisible and infrared portions of spectrum

► The band width is < 10nmThe band width is < 10nm

1mm = 1,0001mm = 1,000m m

11m= 1,000nmm= 1,000nm

http://en.wikipedia.org/wiki/Hyperspectral_imaging

Page 3: Remote Sensing Hyperspectral Remote Sensing

http://en.wikipedia.org/wiki/Hyperspectral_imaging

Page 4: Remote Sensing Hyperspectral Remote Sensing

http://www.csr.utexas.edu/projects/rs/hrs/hyper.html

Vegetation Spectral Reflectance extracted from AVIRIS data

Page 5: Remote Sensing Hyperspectral Remote Sensing

1. Hyperspectral ...1. Hyperspectral ...

► Many features on Earth have diagnostic Many features on Earth have diagnostic spectral characteristics at a resolution of spectral characteristics at a resolution of 20-40nm20-40nm

► Hyperspectral image data can identify Hyperspectral image data can identify these features directly these features directly

► While the traditional multispectral image While the traditional multispectral image data cannotdata cannot

Page 6: Remote Sensing Hyperspectral Remote Sensing

1. Hyperspectral ..1. Hyperspectral ..► Acquires a complete reflectance Acquires a complete reflectance

spectrum for each pixelspectrum for each pixel

► Improves the identification of features Improves the identification of features and quantitatively assess their physical and quantitatively assess their physical and chemical properties and chemical properties

► The target of interests includes The target of interests includes minerals, water, vegetation, soils, and minerals, water, vegetation, soils, and human-made materials human-made materials

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2. History 2. History

AISAIS► Airborne Imaging Spectrometer (AIS) Airborne Imaging Spectrometer (AIS)

developed in 1982 was the first developed in 1982 was the first hyperspectral systemhyperspectral system

► 128 bands, 0.9-2.4128 bands, 0.9-2.4mm

► Designed to identify minerals Designed to identify minerals

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2. History ..2. History ..

AVIRISAVIRIS► Airborne Visible/Infrared Imaging Airborne Visible/Infrared Imaging

Spectrometer was developed in 1987 Spectrometer was developed in 1987

► 224 bands, 0.4-2.5224 bands, 0.4-2.5m, 10nm band widthm, 10nm band width

► The first to cover the visible portion of The first to cover the visible portion of spectrumspectrum

► Provides a large number of images for Provides a large number of images for research and applicationresearch and application

Page 9: Remote Sensing Hyperspectral Remote Sensing

2. History ..2. History .. ► FLI (fluorescence line imager)FLI (fluorescence line imager)► ASAS (Advanced Solid-State Array ASAS (Advanced Solid-State Array

Spectrometer)Spectrometer)► CASI (Compact Airborne Spectrographic CASI (Compact Airborne Spectrographic

Imager)Imager)► HYDICE (hyperspectral digital image HYDICE (hyperspectral digital image

collection experiment)collection experiment)► HyMap (Airborne Hyperspectral HyMap (Airborne Hyperspectral

Scanners)Scanners)► in the 1990’sin the 1990’s

Page 10: Remote Sensing Hyperspectral Remote Sensing

2. History ..2. History .. ► Earth Observing-1 (EO-1)Earth Observing-1 (EO-1)

► The first space borne hyperspectral The first space borne hyperspectral system was launched in 2000 system was launched in 2000

► Developed by NASA and ESA Developed by NASA and ESA

(European Space Agency)(European Space Agency)

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2. History ..2. History .. Earth Observing-1 (EO-1)Earth Observing-1 (EO-1)

► Three instruments are onboard EO-1 Three instruments are onboard EO-1

- Hyperon- Hyperon

220 bands, 0.4-2.5220 bands, 0.4-2.5m, 30m spatial m, 30m spatial

resolution resolution

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http://eo1.gsfc.nasa.gov/miscPages/home.html

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Pearl Harborhttp://eo1.gsfc.nasa.gov/miscPages/home.html

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3. Applications3. Applications► The initial motivation is mineral The initial motivation is mineral

identificationidentification

► Many minerals have unique diagnostic Many minerals have unique diagnostic reflectance characteristicsreflectance characteristics

► Plants are composed of the same few Plants are composed of the same few compounds and should have similar compounds and should have similar spectral signatures spectral signatures

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3. Applications3. Applications► The identification of biochemical and The identification of biochemical and

biophysical characteristics of plants has biophysical characteristics of plants has been a major application areabeen a major application area

► Traditional wide-band multispectral Traditional wide-band multispectral images have limited value in studying images have limited value in studying dominant plant characteristics, such as dominant plant characteristics, such as red absorption, NIR reflectance, and mid red absorption, NIR reflectance, and mid infrared absorptioninfrared absorption

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3. Applications .. 3. Applications .. ► Leaf area index and crown closureLeaf area index and crown closure► Species and compositionSpecies and composition► BiomassBiomass► ChlorophyllChlorophyll► Nutrients, nitrogen, phosphorous, Nutrients, nitrogen, phosphorous,

potassiumpotassium► Leaf and canopy water contentLeaf and canopy water content

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4. Analysis Methods 4. Analysis Methods ► Methods used to extract biochemical Methods used to extract biochemical

and biophysical characteristics from and biophysical characteristics from hyperspectral datahyperspectral data

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4. Analysis Methods ..4. Analysis Methods .. Spectral matchingSpectral matching► Cross-correlagram spectral matching Cross-correlagram spectral matching

(CCSM)(CCSM)

► Taking into consideration the Taking into consideration the correlation coefficient between a target correlation coefficient between a target spectrum and a reference spectrumspectrum and a reference spectrum

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4. Analysis Methods ..4. Analysis Methods ..

Spectral indexSpectral index► Hyperspectral data provide greater chance Hyperspectral data provide greater chance

and flexibility to choose spectral bands and flexibility to choose spectral bands

► Traditional multispectral data only provide Traditional multispectral data only provide the choice of red and NIR bandsthe choice of red and NIR bands

► Narrowband vegetation index to assess Narrowband vegetation index to assess characteristics of bioparameters, characteristics of bioparameters, chlorophyll, foliar chemistry, water, and chlorophyll, foliar chemistry, water, and stress stress

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4. Analysis Methods ..4. Analysis Methods ..

Absorption and spectral positionAbsorption and spectral position► Quantitative assessment of absorption Quantitative assessment of absorption

allows for abundance estimationallows for abundance estimation

► The method measures the depth of valleys The method measures the depth of valleys in a spectral curve to assess absorptionsin a spectral curve to assess absorptions

► and identifies high points in a spectral and identifies high points in a spectral curve to assess spectral position of certain curve to assess spectral position of certain featuresfeatures

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4. Analysis Methods ..4. Analysis Methods ..

Hyperspectral transformation Hyperspectral transformation ► Reduces the data dimensionReduces the data dimension

► Principle Component Analysis (PCA) to Principle Component Analysis (PCA) to reduce the number of bandsreduce the number of bands

► Canonical Discriminant Analysis to Canonical Discriminant Analysis to determine the relationship between determine the relationship between quantitaive variables and nominal classesquantitaive variables and nominal classes

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4. Analysis Methods ..4. Analysis Methods ..

Spectral unmixingSpectral unmixing► The number of bands is much greater than The number of bands is much greater than

the number of endmembersthe number of endmembers

► Statistical methods are used to solve for Fs Statistical methods are used to solve for Fs and Esand Es

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Spectral Mixture Analysis ..Spectral Mixture Analysis ..

► Linear mixture models - assuming a linear Linear mixture models - assuming a linear mixture of pure featuresmixture of pure features

► Endmembers - the pure referenc signaturesEndmembers - the pure referenc signatures

► Weight - the proportion of the area occupied Weight - the proportion of the area occupied by by an endmemberan endmember

► Output - fraction image for each Output - fraction image for each endmember showing the fraction occupied endmember showing the fraction occupied by an endmember in a pixelby an endmember in a pixel

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Spectral Mixture Analysis ..Spectral Mixture Analysis ..

► Two basic conditionsTwo basic conditions

► I. The sum of fractions of all endmembers in I. The sum of fractions of all endmembers in a pixel must equal 1a pixel must equal 1

FFii = F = F11 + F + F22 + … + F + … + Fnn = 1 = 1

► II. The DN of a pixel is the sum of the DNs of II. The DN of a pixel is the sum of the DNs of endmembers weighted by their area endmembers weighted by their area fractionsfractions

DD = F = F1 1 DD11 + F + F2 2 DD22 + … + F + … + Fn n DDnn+E+E

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Spectral Mixture Analysis ..Spectral Mixture Analysis ..

► One DOne Dequation for each band, plus one equation for each band, plus one FFi i

equation for all bandsequation for all bands

► Number of endmembers = number of bands Number of endmembers = number of bands + 1: One exact solution without the E term+ 1: One exact solution without the E term

► Number of endmembers < number of bands Number of endmembers < number of bands +1: Fs and E can be estimated statistically+1: Fs and E can be estimated statistically

► Number of endmembers > number of bands Number of endmembers > number of bands +1: No unique solution+1: No unique solution

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4. Analysis Methods ..4. Analysis Methods ..

Image classificationImage classification► Faces difficulties caused by the high Faces difficulties caused by the high

dimensionality, the high correlation between dimensionality, the high correlation between bands, and a limited number of training bands, and a limited number of training samplessamples

► Requires to maximize the ratio of between-Requires to maximize the ratio of between-class variance and within-class variance of class variance and within-class variance of training samples to separate class centers training samples to separate class centers as far as possibleas far as possible

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4. Analysis Methods ..4. Analysis Methods ..

Empirical analysisEmpirical analysis► Most commonly used methods correlate Most commonly used methods correlate

biophysical/biochemical characteristics with biophysical/biochemical characteristics with spectral reflectance/spectral indices in the spectral reflectance/spectral indices in the visible, NIR, and SWIR wavelengths at leaf, visible, NIR, and SWIR wavelengths at leaf, canopy, or community levelcanopy, or community level

► Simple methods, such as regression, often Simple methods, such as regression, often have higher accuracy, but cannot be applied have higher accuracy, but cannot be applied directly to other areasdirectly to other areas