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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 31–40 of 113 papers

TitleStatusHype
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
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