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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 91–100 of 113 papers

TitleStatusHype
A laboratory-created dataset with ground-truth for hyperspectral unmixing evaluation—0
A Low-rank Tensor Regularization Strategy for Hyperspectral Unmixing—0
A Multibranch Convolutional Neural Network for Hyperspectral Unmixing—0
An Augmented Linear Mixing Model to Address Spectral Variability for Hyperspectral Unmixing—0
An Elliptic Kernel Unsupervised Autoencoder-Graph Convolutional Network Ensemble Model for Hyperspectral Unmixing—0
A spatial compositional model (SCM) for linear unmixing and endmember uncertainty estimation—0
Constrained Nonnegative Matrix Factorization for Blind Hyperspectral Unmixing incorporating Endmember Independence—0
Correntropy Maximization via ADMM - Application to Robust Hyperspectral Unmixing—0
Distributed Machine Learning with Sparse Heterogeneous Data—0
Deep Diffusion Models and Unsupervised Hyperspectral Unmixing for Realistic Abundance Map Synthesis—0
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