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Endmember Variability in hyperspectral image unmixing

Lucas Drumetz 1
1 GIPSA-SIGMAPHY - GIPSA - Signal Images Physique
GIPSA-DIS - Département Images et Signal
Abstract : The fine spectral resolution of hyperspectral remote sensing images allows an accurate analysis of the imaged scene, but due to their limited spatial resolution, a pixel acquired by the sensor is often a mixture of the contributions of several materials. Spectral unmixing aims at estimating the spectra of the pure materials (called endmembers) in the scene, and their abundances in each pixel. The endmembers are usually assumed to be perfectly represented by a single spectrum, which is wrong in practice since each material exhibits a significant intra-class variability. This thesis aims at designing unmixing algorithms to better handle this phenomenon. First, we perform the unmixing locally in well chosen regions of the image where variability effects are less important, and automatically discard wrongly estimated local endmembers using collaborative sparsity. In another approach, we refine the abundance estimation of the materials by taking into account the group structure of an image-derived endmember dictionary. Second, we introduce an extended linear mixing model, based on physical considerations, modeling spectral variability in the form of scaling factors, and develop optimization algorithms to estimate its parameters. This model provides easily interpretable results and outperforms other state-of-the-art approaches. We finally investigate two applications of this model to confirm its relevance.
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Submitted on : Tuesday, April 4, 2017 - 11:32:08 AM
Last modification on : Tuesday, July 6, 2021 - 8:08:02 AM
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  • HAL Id : tel-01394809, version 2


Lucas Drumetz. Endmember Variability in hyperspectral image unmixing. Signal and Image processing. Université Grenoble Alpes, 2016. English. ⟨NNT : 2016GREAT075⟩. ⟨tel-01394809v2⟩



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