Compressed Sensing Based Hyperspectral Unmixing

Date

2014-04-25

Author

Albayrak, R. Tufan
GÜRBÜZ, Ali Cafer
Gunyel, Bertan

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In hyperspectral images the measured spectra for each pixel can be modeled as convex combination of small number of endmember spectra. Since the measured structure contains only a few of possible responses out of possibly many materials sparsity based convex optimization techniques or compressive sensing can be used for hyperspectral unmixing. In this work varying sparsity based techniques are tested for hyperspectral unmixing problem. Performance analysis of these techniques on sparsity level and measurement number are performed. Effect of high coherence of hyperspectral dictionaries is disccussed and effect of signal to noise ratio is analyzed.

Subject Keywords

Hyperspecytral unmixing, Compressive sensing, Sparsity, Convex optimization

URI

https://hdl.handle.net/11511/67069

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Engineering, Conference / Seminar

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Citation Formats

R. T. Albayrak, A. C. GÜRBÜZ, and B. Gunyel, “Compressed Sensing Based Hyperspectral Unmixing,” 2014, p. 1438, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/67069.