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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 1–25 of 113 papers

TitleStatusHype
UnMix-NeRF: Spectral Unmixing Meets Neural Radiance FieldsCode1
Deep Diffusion Models and Unsupervised Hyperspectral Unmixing for Realistic Abundance Map Synthesis—0
Hyperspectral Image Generation with Unmixing Guided Diffusion Model—0
Multitemporal Latent Dynamical Framework for Hyperspectral Images Unmixing—0
A General Framework for Group Sparsity in Hyperspectral Unmixing Using Endmember Bundles—0
Adaptive Multi-Order Graph Regularized NMF with Dual Sparsity for Hyperspectral Unmixing—0
Spectral Unmixing Comparison with Sparse, Iterative and Mixed Integer Programming Models—0
Hyperspectral Unmixing using Iterative, Sparse and Ensambling Approaches for Large Spectral Libraries Applied to Soils and Minerals—0
DTU-Net: A Multi-Scale Dilated Transformer Network for Nonlinear Hyperspectral Unmixing—0
Hyperspectral Unmixing of Agricultural Images taken from UAV Using Adapted U-Net Architecture—0
Unrolling Plug-and-Play Network for Hyperspectral Unmixing—0
Theoretical and Practical Progress in Hyperspectral Pixel Unmixing with Large Spectral Libraries from a Sparse Perspective—0
Investigation of unsupervised and supervised hyperspectral anomaly detection—0
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
Multi-Scale Convolutional Mask Network for Hyperspectral UnmixingCode0
Pixel-to-Abundance Translation: Conditional Generative Adversarial Networks Based on Patch Transformer for Hyperspectral Unmixing—0
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