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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 91–100 of 113 papers

TitleStatusHype
Orthogonal Nonnegative Tucker Decomposition—0
Dynamical Hyperspectral Unmixing with Variational Recurrent Neural NetworksCode0
Learning Interpretable Deep Disentangled Neural Networks for Hyperspectral UnmixingCode0
Hyperspectral Unmixing Using a Neural Network AutoencoderCode0
Dual Simplex Volume Maximization for Simplex-Structured Matrix FactorizationCode0
EndNet: Sparse AutoEncoder Network for Endmember Extraction and Hyperspectral UnmixingCode0
Nonlinear hyperspectral unmixing with robust nonnegative matrix factorizationCode0
Deep Spectral Convolution Network for HyperSpectral UnmixingCode0
Low-Rank Tensor Modeling for Hyperspectral Unmixing Accounting for Spectral VariabilityCode0
Regularization Parameter Selection in Minimum Volume Hyperspectral UnmixingCode0
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