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Proceedings Paper

Unsupervised hyperspectral target analysis
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Paper Abstract

One of the most challenging issues in unsupervised target analysis is how to obtain unknown target knowledge directly from the data to be processed. This issue has never arisen in supervised target analysis where the target knowledge is either assumed to be known or provided by a priori. However, with recent advent of sensor technology many unknown and subtle signal sources can be uncovered and revealed by high spectral imaging spectrometers such as hyperspectral imaging sensors. The knowledge of these signal sources generally cannot be obtained by assumed or prior knowledge. Under this circumstance supervised target analysis may not be realistic or applicable. This paper addresses the issue of how to generate such knowledge for data analysis and further develops unsupervised target finding algorithms for target analysis. In order to demonstrate the utility of the developed unsupervised target finding algorithms, experiments are conducted for applications in unsupervised linear spectral unmixing.

Paper Details

Date Published: 27 August 2008
PDF: 10 pages
Proc. SPIE 7086, Imaging Spectrometry XIII, 70860P (27 August 2008); doi: 10.1117/12.795242
Show Author Affiliations
Xiaoli Jiao, Univ. of Maryland, Baltimore County (United States)
Chein-I Chang, Univ. of Maryland, Baltimore County (United States)


Published in SPIE Proceedings Vol. 7086:
Imaging Spectrometry XIII
Sylvia S. Shen; Paul E. Lewis, Editor(s)

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