Share Email Print
cover

Journal of Applied Remote Sensing

Hyperspectral image analysis using artificial color
Author(s): Jian Fu; H. John Caulfield; Dongsheng Wu; Wubishet Tadesse
Format Member Price Non-Member Price
PDF $20.00 $25.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

By definition, HSC (HyperSpectral Camera) images are much richer in spectral data than, say, a COTS (Commercial-Off-The-Shelf) color camera. But data are not information. If we do the task right, useful information can be derived from the data in HSC images. Nature faced essentially the identical problem. The incident light is so complex spectrally that measuring it with high resolution would provide far more data than animals can handle in real time. Nature's solution was to do irreversible POCS (Projections Onto Convex Sets) to achieve huge reductions in data with minimal reduction in information. Thus we can arrange for our manmade systems to do what nature did - project the HSC image onto two or more broad, overlapping curves. The task we have undertaken in the last few years is to develop this idea that we call Artificial Color. What we report here is the use of the measured HSC image data projected onto two or three convex, overlapping, broad curves in analogy with the sensitivity curves of human cone cells. Testing two quite different HSC images in that manner produced the desired result: good discrimination or segmentation that can be done very simply and hence are likely to be doable in real time with specialized computers. Using POCS on the HSC data to reduce the processing complexity produced excellent discrimination in those two cases. For technical reasons discussed here, the figures of merit for the kind of pattern recognition we use is incommensurate with the figures of merit of conventional pattern recognition. We used some force fitting to make a comparison nevertheless, because it shows what is also obvious qualitatively. In our tasks our method works better.

Paper Details

Date Published: 1 March 2010
PDF: 16 pages
J. Appl. Remote Sens. 4(1) 043514 doi: 10.1117/1.3374451
Published in: Journal of Applied Remote Sensing Volume 4, Issue 1
Show Author Affiliations
Jian Fu, Alabama A&M Univ. (United States)
H. John Caulfield, Alabama A&M Univ. (United States)
Dongsheng Wu, The Univ. of Alabama in Huntsville (United States)
Wubishet Tadesse, Alabama A&M Univ. (United States)


© SPIE. Terms of Use
Back to Top