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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 41–50 of 113 papers

TitleStatusHype
A Data Dependent Multiscale Model for Hyperspectral Unmixing With Spectral Variability—0
Hyperspectral Unmixing Based on Nonnegative Matrix Factorization: A Comprehensive Review—0
Deep Learning-Based Correction and Unmixing of Hyperspectral Images for Brain Tumor Surgery—0
Hyperspectral Unmixing Based on Clustered Multitask Networks—0
A Multibranch Convolutional Neural Network for Hyperspectral Unmixing—0
Hyperspectral unmixing for Raman spectroscopy via physics-constrained autoencoders—0
Hyperspectral Image Generation with Unmixing Guided Diffusion Model—0
Hyperspectral Unmixing Network Inspired by Unfolding an Optimization Problem—0
Deep Diffusion Models and Unsupervised Hyperspectral Unmixing for Realistic Abundance Map Synthesis—0
HYPERION: Hyperspectral Penetrating-type Ellipsoidal Reconstruction for Terahertz Blind Source Separation—0
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