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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
Hyperspectral Unmixing Under Endmember Variability: A Variational Inference Framework—0
An Elliptic Kernel Unsupervised Autoencoder-Graph Convolutional Network Ensemble Model for Hyperspectral Unmixing—0
Temperature scaling unmixing framework based on convolutional autoencoderCode0
Semi-NMF Regularization-Based Autoencoder Training for Hyperspectral UnmixingCode0
Dual Simplex Volume Maximization for Simplex-Structured Matrix FactorizationCode0
Hyperspectral unmixing for Raman spectroscopy via physics-constrained autoencoders—0
Transformer based Endmember Fusion with Spatial Context for Hyperspectral Unmixing—0
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
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