Unmixing and Target Recognition in Airborne Hyper-Spectral Images

Amir Averbuch, Michael Zheludev, Valery Zheludev

Abstract


We present two new linear algorithms that perform unmixing in hyper-spectral images and then recognize their targets whose spectral signatures are given. The first algorithm is based on the ordered topology of spectral signatures. The second algorithm is based on a linear decomposition of each pixel's neighborhood. The sought after target can occupy sub- or above pixel. These algorithms combine ideas from algebra and probability theories as well as statistical data mining. Experimental results demonstrate their robustness. This paper is a complementary extension to Averbuch & Zheludev (2012).

Full Text: PDF DOI: 10.5539/esr.v1n2p200

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Earth Science Research   ISSN 1927-0542 (Print)   ISSN 1927-0550 (Online)
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