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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 101–110 of 113 papers

TitleStatusHype
Deep Learning-Based Correction and Unmixing of Hyperspectral Images for Brain Tumor Surgery—0
Deep Nonlinear Hyperspectral Unmixing Using Multi-task Learning—0
DTU-Net: A Multi-Scale Dilated Transformer Network for Nonlinear Hyperspectral Unmixing—0
Effective Spectral Unmixing via Robust Representation and Learning-based Sparsity—0
Transformer based Endmember Fusion with Spatial Context for Hyperspectral Unmixing—0
Enhancing Pure-Pixel Identification Performance via Preconditioning—0
Extracting Optimal Solution Manifolds using Constrained Neural Optimization—0
Fast and Robust Recursive Algorithms for Separable Nonnegative Matrix Factorization—0
Fast and Structured Block-Term Tensor Decomposition For Hyperspectral Unmixing—0
GAUSS: Guided Encoder-Decoder Architecture for Hyperspectral Unmixing with Spatial Smoothness—0
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