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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 26–50 of 113 papers

TitleStatusHype
Hyperspectral unmixing for Raman spectroscopy via physics-constrained autoencoders—0
Transformer based Endmember Fusion with Spatial Context for Hyperspectral Unmixing—0
Deep Learning-Based Correction and Unmixing of Hyperspectral Images for Brain Tumor Surgery—0
Deep Nonlinear Hyperspectral Unmixing Using Multi-task Learning—0
Multilayer Simplex-structured Matrix Factorization for Hyperspectral Unmixing with Endmember Variability—0
Multi-Scale Convolutional Mask Network for Hyperspectral UnmixingCode0
Pixel-to-Abundance Translation: Conditional Generative Adversarial Networks Based on Patch Transformer for Hyperspectral Unmixing—0
SpACNN-LDVAE: Spatial Attention Convolutional Latent Dirichlet Variational Autoencoder for Hyperspectral Pixel Unmixing—0
MultiHU-TD: Multifeature Hyperspectral Unmixing Based on Tensor DecompositionCode0
Learning Interpretable Deep Disentangled Neural Networks for Hyperspectral UnmixingCode0
Hyperspectral Blind Unmixing using a Double Deep Image PriorCode0
AE-RED: A Hyperspectral Unmixing Framework Powered by Deep Autoencoder and Regularization by Denoising—0
SAWU-Net: Spatial Attention Weighted Unmixing Network for Hyperspectral Images—0
Dynamical Hyperspectral Unmixing with Variational Recurrent Neural NetworksCode0
Nonlinear Hyperspectral Unmixing based on Multilinear Mixing Model using Convolutional Autoencoders—0
Towards Robust Hyperspectral Unmixing: Mixed Noise Modeling and Image-Domain RegularizationCode0
Variable-Wise Diagonal Preconditioning for Primal-Dual Splitting: Design and Applications—0
A Multibranch Convolutional Neural Network for Hyperspectral Unmixing—0
A consistent and flexible framework for deep matrix factorizations—0
Hyperspectral Unmixing Based on Nonnegative Matrix Factorization: A Comprehensive Review—0
Fast and Structured Block-Term Tensor Decomposition For Hyperspectral Unmixing—0
GAUSS: Guided Encoder-Decoder Architecture for Hyperspectral Unmixing with Spatial Smoothness—0
Spectral Unmixing of Hyperspectral Images Based on Block Sparse Structure—0
SSCU-Net: Spatial-Spectral Collaborative Unmixing Network for Hyperspectral Images—0
Deep Deterministic Independent Component Analysis for Hyperspectral UnmixingCode0
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