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Hyperspectral Unmixing

Hyperspectral Unmixing is a procedure that decomposes the measured pixel spectrum of hyperspectral data into a collection of constituent spectral signatures (or endmembers) and a set of corresponding fractional abundances. Hyperspectral Unmixing techniques have been widely used for a variety of applications, such as mineral mapping and land-cover change detection.

Source: An Augmented Linear Mixing Model to Address Spectral Variability for Hyperspectral Unmixing

Papers

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Showing 61–70 of 113 papers

TitleStatusHype
Extracting Optimal Solution Manifolds using Constrained Neural Optimization—0
Superpixel Based Graph Laplacian Regularization for Sparse Hyperspectral Unmixing—0
Multi-Resolution Beta-Divergence NMF for Blind Spectral Unmixing—0
Hyperspectral Unmixing Network Inspired by Unfolding an Optimization Problem—0
Constrained Nonnegative Matrix Factorization for Blind Hyperspectral Unmixing incorporating Endmember Independence—0
Distributed Machine Learning with Sparse Heterogeneous Data—0
Orthogonal Nonnegative Tucker Decomposition—0
Regularization Parameter Selection in Minimum Volume Hyperspectral UnmixingCode0
Successive Projection Algorithm Robust to Outliers—0
Matrix cofactorization for joint spatial-spectral unmixing of hyperspectral images—0
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