IJEEDC Sparse Unmixing Using Robust Greedy Algorithm For Heperspectral Data

Journal Paper

Paper Title :Sparse Unmixing Using Robust Greedy Algorithm For Heperspectral Data

Author :Shwetha K M, Umarani T G

Article Citation :Shwetha K M ,Umarani T G , (2015 ) " Sparse Unmixing Using Robust Greedy Algorithm For Heperspectral Data " , International Journal of Electrical, Electronics and Data Communication (IJEEDC) , pp. 36-39, Volume-3, Issue-4

Abstract : Sparse unmixing assumes that each observed signature of a hyperspectral image is a linear combination of only a few spectra (endmembers) in an available spectral library. It then estimates the fractional abundances of these endmembers(materials) in the scene. Sparse unmixing problem still remains a great difficulty due to the usually high correlation of the spectral library. Under such circumstances, this paper presents a novel greedy algorithm for sparse unmixing of hyperspectral data.This algorithm has low computational complexity of getting an approximate solution for the l0 problem directly and can exploit the joint sparsity among all the pixels in the hyperspectral data. Besides, the combination of forward greedy step and backward greedy step makes this algorithm more stable and less likely to be trapped into the local optimum than the conventional greedy algorithms. Furthermore, when updating the solution in each iteration, a regularizer that enforces the spatial-contextual coherence within the hyperspectral image is considered to make the algorithm more effective.Experimental results on both synthetic data and real data demonstrate the effectiveness of the proposed algorithm. Keywords— Hyperspectral imaging, Hyperspetral unmixing, Sparse unmixing, Spectral library, Greedy Algorithm(GA).

Type : Research paper

Published : Volume-3, Issue-4


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Feb. 2025
Submitted Papers	:	80
Accepted Papers	:	10
Rejected Papers	:	70
Acc. Perc	:	12%
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Published : Volume-3, Issue-4

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