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 1  m= 1,000nm

Vegetation Spectral Reflectance extracted from AVIRIS data

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

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

2. History AIS AIS ► Airborne Imaging Spectrometer (AIS) developed in 1982 was the first hyperspectral system ► 128 bands,  m ► Designed to identify minerals

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

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

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) (European Space Agency)

2. History.. Earth Observing-1 (EO-1) ► Three instruments are onboard EO-1 - Hyperon - Hyperon 220 bands,  m, 30m spatial 220 bands,  m, 30m spatial resolution resolution

Pearl Harbor

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

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

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

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

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

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

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

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

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

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

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

Spectral Mixture Analysis.. ► One D   equation for each band, plus one  F i 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

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

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