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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 21–30 of 113 papers

TitleStatusHype
Deep Learning-Based Correction and Unmixing of Hyperspectral Images for Brain Tumor Surgery—0
Deep Nonlinear Hyperspectral Unmixing Using Multi-task Learning—0
Multilayer Simplex-structured Matrix Factorization for Hyperspectral Unmixing with Endmember Variability—0
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
Image Processing and Machine Learning for Hyperspectral Unmixing: An Overview and the HySUPP Python PackageCode1
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