Hyperspectral Imagery Remote Sensing Hyperspectral Imagery April 1st, 2004 Stefan A. Robila robilas@mail.montclair.edu www.csam.montclair.edu/~robila/RSL/ Source: http://nis-www.lanl.gov/~borel/

Hyperspectral Remote Sensing • a remote sensing technology • “seeing” characteristics not recognized by the human eye Electromagnetic Spectrum Increasing Wavelength (in meters) 10 -8 Ultraviolet Microwaves 10 -2 10 -6 Infrared 10 -11 Gamma Rays 10 Radio X-Rays 10 -9 Visible 10 -7

Non-Imaging Instruments Hyperspectral Remote Sensing Non-Imaging Instruments (example: FieldSpec Hand Held Spectroradiometer) • sensor obtains data (amount of light per wavelength) • computer software displays recorded spectrum • analyze spectral signature

Imaging systems Scanning radiometers Passive system Produces digital images

Imaging systems Scanning radiometers Mirror scans across-track (swath)

Imaging Systems Scanning radiometers 2-D image formed by platform forward motion

CCD CCD arrays Passive system Line or block of CCDs instead of scanning mirror Senses entire swath (or block) simultaneously

Hyperspectral Remote Sensing Multispectral – Many spectra (bands) Hyperspectral – Huge numbers of continuous bands Hyperspectral remote sensing provides a continuous, essentially complete record of spectral responses of materials over the wavelengths considered.

Hyperspectral Platforms First hyperspectral scanners: 1982: AIS (Airborne Imaging Spectrometer) 1987: AVIRIS (Airborne Visible/infrared Imaging Spectrometer) 1995: Hyperspectral Digital Imagery Collection Experiment (HYDICE) 2000: Hyperion (EO-1)

AVIRIS

AVIRIS Specifications 224 individual CCD (charge coupled device) detectors Spectral resolution of 10 nanometers Spatial resolution of 20 meters (at typical flight altitude) Flight platform: NASA ER-2 (modified U-2) Flight altitude: from 20,000 to 60,000, but usually flown at 60,000 Typical swath width is 11 km. Dispersion of the spectrum against the detector array is accomplished with a diffraction grating. The total interval reaches from 380 to 2500 nanometers (roughly the same as TM band range). image, pushbroom-like, succession of lines, each containing 664 pixels.

Hyperspectral Cube • shows the volume of data returned by imaging instruments • illustrates how data from imaging instruments is geo-referenced • data from different wavelengths can be used to create a “map” (in either true color or false color infrared formats)

Hyperspectral Remote Sensing Hyperspectral images can be analyzed in ways that multispectral images cannot In the Visible-NIR range, water ice and dry ice give characteristic spectral curves, as shown here:

Hyperspectral Data Analysis General Approach: Develop Spectral Library Construct spectral curve for relatively "pure" materials Specific reflectance peaks and absorption troughs are read from these curves. Compare to lab spectra (mixture analysis) Mixtures of two or even three different materials can be identified as the components of the compound spectral curve.

Hyperspectral Data Analysis Spectral Libraries: Sets of hundreds of measured spectra for components likely to be encountered in the study area.

The distance measure used for spectral screening. Spectral Angle The distance measure used for spectral screening. For two pixel vectors x and y, the spectral angle is computed as:

Hyperspectral Data Analysis Pure Pixel Analysis Find relatively “pure” pixels Pixel Purity Index (PPI) “Pure” spectra are spectral endmembers Endmembers Spectral characteristics of an image that represent classes of interest Usually assigned based on lab spectra Can be done manually

Hyperspectral Data Analysis Spectral Mixture Analysis (SMA) Also called “unmixing” Assumes that the reflectance spectrum derived from sensor can be deconvolved into a linear mixture of the spectra of ground components Linear / Non-linear Linear SMA assumes linear relationship between reflectance and area

Linear Mixture Model Each pixel vector x can be described as: where S is the nxm matrix of spectra (s1, .., sm) of the individual materials (also called endmembers), a is an m-dimensional abundance vector and w is the additive noise vector. The abundances of the endmembers have the restrictions: The ICA performs endmember unmixing; the resulting components correspond to the abundances of the endmembers, the columns in the mixing matrix correspond to the endmembers.

Future Hyperspectral Sensors Spaceborne rather than airborne Success: Hyperion, is part of NASA’s EO-1 - launched in December, 2000. Co-orbiting with Landsat 7 220 channels from 400 to 2500 nm Ground resolution 30 meters.

Future Hyperspectral Sensors Hyperion

Future Hyperspectral Sensors Off-the shelf (reduce costs) Success: SOC 700 (Surface Optics) Spectral Band: 0.43 –to 0.9 microns Number of Bands: 120, 240 or 480 (configurable) Dynamic Range: 12-bit Line Rate: Up to 100 lines/second (120 bands) Pixels per line: 640 Exposure Time: 10 -> 10^7 microsecond

Hyperspectral Problems Data volume Cost Difficulty of analysis Spectral Libraries More complex