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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 1–10 of 113 papers

TitleStatusHype
Image Processing and Machine Learning for Hyperspectral Unmixing: An Overview and the HySUPP Python PackageCode1
Deep Hyperspectral Unmixing using Transformer NetworkCode1
Entropic Descent Archetypal Analysis for Blind Hyperspectral UnmixingCode1
Hyperspectral Image Super-resolution via Deep Progressive Zero-centric Residual LearningCode1
Integration of Physics-Based and Data-Driven Models for Hyperspectral Image UnmixingCode1
UnMix-NeRF: Spectral Unmixing Meets Neural Radiance FieldsCode1
Differentiable Programming for Hyperspectral Unmixing using a Physics-based Dispersion ModelCode1
Endmember-Guided Unmixing Network (EGU-Net): A General Deep Learning Framework for Self-Supervised Hyperspectral UnmixingCode1
Dual Simplex Volume Maximization for Simplex-Structured Matrix FactorizationCode0
A Gaussian mixture model representation of endmember variability in hyperspectral unmixingCode0
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