1 / 25

Remote Sensing Hyperspectral Imagery April 1 st , 2004 Stefan A. Robila robilas@mail.montclair

Remote Sensing Hyperspectral Imagery April 1 st , 2004 Stefan A. Robila robilas@mail.montclair.edu www.csam.montclair.edu/~robila/RSL/. Source: http://nis-www.lanl.gov/~borel/. Increasing Wavelength (in meters). 10 -6 Infrared. 10 -11 Gamma Rays. 10 -8 Ultraviolet. 10 Radio.

kuame-wong
Download Presentation

Remote Sensing Hyperspectral Imagery April 1 st , 2004 Stefan A. Robila robilas@mail.montclair

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. 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/

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

  3. 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

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

  8. 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.

  9. 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)

  10. AVIRIS

  11. 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.

  12. 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)

  13. 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:

  14. 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.

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

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

  17. 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

  18. 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

  19. Linear Mixture Model • Each pixel vector x can be described as: • whereS 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.

  20. 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.

  21. Future Hyperspectral Sensors Hyperion

  22. 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

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

More Related