Hyperspectral Unmixing
26 papers with code • 0 benchmarks • 0 datasets
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
Benchmarks
These leaderboards are used to track progress in Hyperspectral Unmixing
Most implemented papers
Dynamical Hyperspectral Unmixing with Variational Recurrent Neural Networks
First, a stochastic model is proposed to represent both the dynamical evolution of the endmembers and their abundances, as well as the mixing process.
Image Processing and Machine Learning for Hyperspectral Unmixing: An Overview and the HySUPP Python Package
Additionally, we draw a critical comparison between advanced and conventional techniques from the three categories.
Hyperspectral Blind Unmixing using a Double Deep Image Prior
ChaoEdisonZhouUCL/BUDDIP-TNNLS
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IEEE Transactions on Neural Networks and Learning Systems 2023
With the rise of machine learning, hyperspectral image (HSI) unmixing problems have been tackled using learning-based methods.
Learning Interpretable Deep Disentangled Neural Networks for Hyperspectral Unmixing
ricardoborsoi/IDNet_release
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The model is learned end-to-end using stochastic backpropagation, and trained using a self-supervised strategy which leverages benefits from semi-supervised learning techniques.
Dual Simplex Volume Maximization for Simplex-Structured Matrix Factorization
Simplex-structured matrix factorization (SSMF) is a generalization of nonnegative matrix factorization, a fundamental interpretable data analysis model, and has applications in hyperspectral unmixing and topic modeling.
Semi-NMF Regularization-Based Autoencoder Training for Hyperspectral Unmixing
dv-fenix/SemiNMF-Autoencoders
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30th National Conference on Communications (NCC) 2024
Hyperspectral Unmixing (HSU) refers to the procedure of decomposing measured pixel spectra into a set of constituent spectral signatures known as endmembers and a corresponding set of fractional mixing ratios.
