1. Hyperspectral Imagery
Remote Sensing
Hyperspectral Imagery
April 1st, 2004
Stefan A. Robila
Source:

2. 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

3. 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

4. Imaging systems Scanning radiometers Passive system
Produces digital images

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

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

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

9. 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.

10. 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)

11. AVIRIS

12. 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.

14. 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)

15. 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:

16. 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.

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

18. 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:

19. 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

20. 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

21. 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.

22. 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.

23. Future Hyperspectral Sensors
Hyperion

24. 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

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