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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 51–75 of 113 papers

TitleStatusHype
On Hyperspectral Unmixing—0
Identifiability-Guaranteed Simplex-Structured Post-Nonlinear Mixture Learning via Autoencoder—0
Endmember-Guided Unmixing Network (EGU-Net): A General Deep Learning Framework for Self-Supervised Hyperspectral UnmixingCode1
Model-Based Deep Autoencoder Networks for Nonlinear Hyperspectral Unmixing—0
Probabilistic Simplex Component Analysis—0
SCA-Net: A Self-Correcting Two-Layer Autoencoder for Hyper-spectral Unmixing—0
A Plug-and-Play Priors Framework for Hyperspectral UnmixingCode0
Hyperspectral Unmixing via Nonnegative Matrix Factorization with Handcrafted and Learnt Priors—0
Extracting Optimal Solution Manifolds using Constrained Neural Optimization—0
Superpixel Based Graph Laplacian Regularization for Sparse Hyperspectral Unmixing—0
Differentiable Programming for Hyperspectral Unmixing using a Physics-based Dispersion ModelCode1
Multi-Resolution Beta-Divergence NMF for Blind Spectral Unmixing—0
Hyperspectral Image Super-resolution via Deep Progressive Zero-centric Residual LearningCode1
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
Generalized Separable Nonnegative Matrix Factorization—0
Hyperspectral Unmixing via Deep Autoencoder Networks for a Generalized Linear-Mixture/Nonlinear-Fluctuation Model—0
A Dual Symmetric Gauss-Seidel Alternating Direction Method of Multipliers for Hyperspectral Sparse Unmixing—0
A laboratory-created dataset with ground-truth for hyperspectral unmixing evaluation—0
Improved Hyperspectral Unmixing With Endmember Variability Parametrized Using an Interpolated Scaling Tensor—0
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