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Proceedings Paper

Hyperspectral analysis toolset
Author(s): James N. Sweet; Mary H. Sharp; James C. Granahan
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Paper Abstract

12 Hyperspectral images are becoming more common and have considerable information content. Analysis tools must keep up with the changing demands and opportunities posed by the new datasets. Traditional tools such as image compression, and classification, both supervised and unsupervised, can be improved. Newly developed tools will enhance the commercial value of the data (e.g. tools capable of objective data cube quality evaluation). This paper discusses several new or improved analysis tools developed for use with hyperspectral images. The algorithm fundamental to many of these tools is the Spectral Similarity Scale (SSS). The SSS is an objective measure of spectral distance that quantifies differences in both magnitude (albedo) and direction (shape). This is a fundamental improvement in the description of distance between two spectra. The toolset described in this paper consists of: 1) image quality evaluation, which is an objective measurement of information content, image complexity, and subtlety; 2) hyperspectral image compression, which facilitates image storage and transmission; 3) processing-induced spectral change objectively quantifies spectral changes caused by image processing such as lossy compression. Using the SSS as the measure of spectral distance improves the performance of both supervised classification and unsupervised classification algorithms.

Paper Details

Date Published: 9 February 2001
PDF: 12 pages
Proc. SPIE 4169, Sensors, Systems, and Next-Generation Satellites IV, (9 February 2001); doi: 10.1117/12.417150
Show Author Affiliations
James N. Sweet, BAE Systems (United States)
Mary H. Sharp, BAE Systems (United States)
James C. Granahan, BAE Systems (United States)


Published in SPIE Proceedings Vol. 4169:
Sensors, Systems, and Next-Generation Satellites IV
Hiroyuki Fujisada; Joan B. Lurie; Alexander Ropertz; Konradin Weber, Editor(s)

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