<title>Utilizing subpixel identification schemes to address emerging application areas</title>

Abstract

The process of extracting information from hyperspectral imagery datasets provided by newer sensor systems can be enhanced through a combination of unique spectral processing algorithms. The first technique we describe is a unique method for extracting the relevant bands within a hyperspectral dataset; this set of optimized bands will provide the greatest potential for discriminating specified materials of interest. The second process, subpixel spectral identification, uses the results from the subset of hyperspectral bands to further refine and distinguish between specific materials of interest, improving classification accuracy and diminishing false alarms. Comparison results produced using the full hyperspectral bandset, a six-band selection chosen based on thematic-mapper band centers, and the optimized bandset are presented for a test scene using HYDICE hyperspectral imagery.