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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
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
Entropic Descent Archetypal Analysis for Blind Hyperspectral UnmixingCode1
A Multibranch Convolutional Neural Network for Hyperspectral Unmixing—0
A consistent and flexible framework for deep matrix factorizations—0
Integration of Physics-Based and Data-Driven Models for Hyperspectral Image UnmixingCode1
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