Boosting Hyperspectral Image Unmixing Using Denoising: Four Scenarios

Behnood Rasti, Bikram Koirala, Paul Scheunders, Pedram Ghamisi, and Richard Gloaguen

We present an analysis of the influence of noise on the unmixing of hyperspectral data. We propose four scenarios to 1) investigate the effect of noise reduction as a preprocessing
step on the performance of hyperspectral unmixing and 2) study the relation between noise and different endmembers selection strategies. Experiments are conducted on a simulated and a real datasets with a wide range of signal to noise ratios (from 10 to 50 dB).

2021 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 3821-3824, 2021-07-11.

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