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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
Orthogonal Nonnegative Tucker Decomposition—0
Pixel-to-Abundance Translation: Conditional Generative Adversarial Networks Based on Patch Transformer for Hyperspectral Unmixing—0
Probabilistic Simplex Component Analysis—0
Robust Hyperspectral Unmixing with Correntropy based Metric—0
SAWU-Net: Spatial Attention Weighted Unmixing Network for Hyperspectral Images—0
SCA-Net: A Self-Correcting Two-Layer Autoencoder for Hyper-spectral Unmixing—0
Self-Dictionary Sparse Regression for Hyperspectral Unmixing: Greedy Pursuit and Pure Pixel Search are Related—0
Semiblind Hyperspectral Unmixing in the Presence of Spectral Library Mismatches—0
Semidefinite Programming Based Preconditioning for More Robust Near-Separable Nonnegative Matrix Factorization—0
Semi-Supervised Endmember Identification In Nonlinear Spectral Mixtures Via Semantic Representation—0
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