A Review of Unsupervised Spectral Target Analysis for Hyperspectral Imagery

Autor: Yingzi Du, Mann-Li Chang, Chao-Cheng Wu, Xiaoli Jiao, Chein-I Chang
Jazyk: angličtina
Rok vydání: 2010
Předmět:
Zdroj: EURASIP Journal on Advances in Signal Processing, Vol 2010 (2010)
Druh dokumentu: article
ISSN: 1687-6172
1687-6180
DOI: 10.1155/2010/503752
Popis: One of great challenges in unsupervised hyperspectral target analysis is how to obtain desired knowledge in an unsupervised means directly from the data for image analysis. This paper provides a review of unsupervised target analysis by first addressing two fundamental issues, “what are material substances of interest, referred to as targets?” and “how can these targets be extracted from the data?” and then further developing least squares (LS)-based unsupervised algorithms for finding spectral targets for analysis. In order to validate and substantiate the proposed unsupervised hyperspectral target analysis, three applications in endmember extraction, target detection and linear spectral unmixing are considered where custom-designed synthetic images and real image scenes are used to conduct experiments.
Databáze: Directory of Open Access Journals
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