Volume 13, Issue 3 (12-2016)                   JSDP 2016, 13(3): 155-169 | Back to browse issues page

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Fayyazi H, Dehghani H, Hosseini M. Sparse unmixing of hyper-spectral images using a pruned spectral library. JSDP. 2016; 13 (3) :155-169
URL: http://jsdp.rcisp.ac.ir/article-1-128-en.html
MSc Malek-Ashtar University of Technology
Abstract:   (1022 Views)

Spectral unmixing of hyperspectral images is one of the most important research fields  in remote sensing. Recently, the direct use of spectral libraries in spectral unmixing is on increase. In this way  which is called sparse unmixing, we do not need an endmember extraction algorithm and the number determination of endmembers priori. Since spectral libraries usually contain highly correlated spectra, the sparse unmixing approach leads to non-admissible solutions. On the other hand, most of the proposed solutions are not noise-resistant and do not reach to a sufficiently high sparse solution. In this paper, with the purpose of overcoming the problems above, at first the spectral library will be pruned based on the spectral information of the image,clustering and classification techniques. Then a genetic algorithm  will be used for sparse unmixing. The experimental results on the simulated and real images show that the proposed method gives good results in noisy images. 

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Type of Study: Research | Subject: Paper
Received: 2013/06/23 | Accepted: 2016/07/26 | Published: 2017/04/23 | ePublished: 2017/04/23

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