Remote Sensing Hyperspectral Remote Sensing

1. Remote SensingHyperspectral Remote Sensing

2. 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 = 1,000mm 1mm= 1,000nm http://en.wikipedia.org/wiki/Hyperspectral_imaging

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

4. Vegetation Spectral Reflectance extracted from AVIRIS data http://www.csr.utexas.edu/projects/rs/hrs/hyper.html

5. 1. Hyperspectral ... • Many features on Earth have diagnostic spectral characteristics at a resolution of 20-40nm • Hyperspectral image data can identify these features directly • While the traditional multispectral image data cannot

6. 1. Hyperspectral .. • Acquires a complete reflectance spectrum for each pixel • Improves the identification of features and quantitatively assess their physical and chemical properties • The target of interests includes minerals, water, vegetation, soils, and human-made materials

7. 2. History AIS • Airborne Imaging Spectrometer (AIS) developed in 1982 was the first hyperspectral system • 128 bands, 0.9-2.4mm • Designed to identify minerals

8. 2. History .. AVIRIS • Airborne Visible/Infrared Imaging Spectrometer was developed in 1987 • 224 bands, 0.4-2.5mm, 10nm band width • The first to cover the visible portion of spectrum • Provides a large number of images for research and application

9. 2. History .. • FLI (fluorescence line imager) • ASAS (Advanced Solid-State Array Spectrometer) • CASI (Compact Airborne Spectrographic Imager) • HYDICE (hyperspectral digital image collection experiment) • HyMap (Airborne Hyperspectral Scanners) • in the 1990’s

10. 2. History .. • Earth Observing-1 (EO-1) • The first space borne hyperspectral system was launched in 2000 • Developed by NASA and ESA (European Space Agency)

11. 2. History .. Earth Observing-1 (EO-1) • Three instruments are onboard EO-1 - Hyperon 220 bands, 0.4-2.5mm, 30m spatial resolution

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

13. Pearl Harbor http://eo1.gsfc.nasa.gov/miscPages/home.html

14. 3. Applications • The initial motivation is mineral identification • Many minerals have unique diagnostic reflectance characteristics • Plants are composed of the same few compounds and should have similar spectral signatures

15. 3. Applications • The identification of biochemical and biophysical characteristics of plants has been a major application area • Traditional wide-band multispectral images have limited value in studying dominant plant characteristics, such as red absorption, NIR reflectance, and mid infrared absorption

16. 3. Applications .. • Leaf area index and crown closure • Species and composition • Biomass • Chlorophyll • Nutrients, nitrogen, phosphorous, potassium • Leaf and canopy water content

17. 4. Analysis Methods • Methods used to extract biochemical and biophysical characteristics from hyperspectral data

18. 4. Analysis Methods .. Spectral matching • Cross-correlagram spectral matching (CCSM) • Taking into consideration the correlation coefficient between a target spectrum and a reference spectrum

19. 4. Analysis Methods .. Spectral index • Hyperspectral data provide greater chance and flexibility to choose spectral bands • Traditional multispectral data only provide the choice of red and NIR bands • Narrowband vegetation index to assess characteristics of bioparameters, chlorophyll, foliar chemistry, water, and stress

20. 4. Analysis Methods .. Absorption and spectral position • Quantitative assessment of absorption allows for abundance estimation • The method measures the depth of valleys in a spectral curve to assess absorptions • and identifies high points in a spectral curve to assess spectral position of certain features

21. 4. Analysis Methods .. Hyperspectral transformation • Reduces the data dimension • Principle Component Analysis (PCA) to reduce the number of bands • Canonical Discriminant Analysis to determine the relationship between quantitaive variables and nominal classes

22. 4. Analysis Methods .. Spectral unmixing • The number of bands is much greater than the number of endmembers • Statistical methods are used to solve for Fs and Es

23. Spectral Mixture Analysis .. Linear mixture models - assuming a linear mixture of pure features Endmembers - the pure referenc signatures Weight - the proportion of the area occupied by an endmember Output - fraction image for each endmember showing the fraction occupied by an endmember in a pixel

24. Spectral Mixture Analysis .. Two basic conditions I. The sum of fractions of all endmembers in a pixel must equal 1 Fi = F1 + F2 + … + Fn = 1 II. The DN of a pixel is the sum of the DNs of endmembers weighted by their area fractions D = F1 D1 + F2 D2 + … + Fn Dn+E

25. Spectral Mixture Analysis .. One Dequation for each band, plus one Fi equation for all bands Number of endmembers = number of bands + 1: One exact solution without the E term Number of endmembers < number of bands +1: Fs and E can be estimated statistically Number of endmembers > number of bands +1: No unique solution

26. 4. Analysis Methods .. Image classification • Faces difficulties caused by the high dimensionality, the high correlation between bands, and a limited number of training samples • Requires to maximize the ratio of between-class variance and within-class variance of training samples to separate class centers as far as possible

27. 4. Analysis Methods .. Empirical analysis • Most commonly used methods correlate biophysical/biochemical characteristics with spectral reflectance/spectral indices in the visible, NIR, and SWIR wavelengths at leaf, canopy, or community level • Simple methods, such as regression, often have higher accuracy, but cannot be applied directly to other areas