Unmixing and Target Recognition in Airborne Hyper-Spectral Images

  •  Amir Averbuch    
  •  Michael Zheludev    
  •  Valery Zheludev    


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).

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