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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 51–75 of 113 papers

TitleStatusHype
Effective Spectral Unmixing via Robust Representation and Learning-based Sparsity—0
Transformer based Endmember Fusion with Spatial Context for Hyperspectral Unmixing—0
Enhancing Pure-Pixel Identification Performance via Preconditioning—0
Extracting Optimal Solution Manifolds using Constrained Neural Optimization—0
Fast and Robust Recursive Algorithms for Separable Nonnegative Matrix Factorization—0
Fast and Structured Block-Term Tensor Decomposition For Hyperspectral Unmixing—0
GAUSS: Guided Encoder-Decoder Architecture for Hyperspectral Unmixing with Spatial Smoothness—0
Generalized Separable Nonnegative Matrix Factorization—0
HYPERION: Hyperspectral Penetrating-type Ellipsoidal Reconstruction for Terahertz Blind Source Separation—0
Hyperspectral Image Generation with Unmixing Guided Diffusion Model—0
Hyperspectral Unmixing Based on Clustered Multitask Networks—0
Hyperspectral Unmixing Based on Nonnegative Matrix Factorization: A Comprehensive Review—0
Hyperspectral unmixing for Raman spectroscopy via physics-constrained autoencoders—0
Hyperspectral Unmixing: Ground Truth Labeling, Datasets, Benchmark Performances and Survey—0
Hyperspectral Unmixing Network Inspired by Unfolding an Optimization Problem—0
Hyperspectral Unmixing of Agricultural Images taken from UAV Using Adapted U-Net Architecture—0
Hyperspectral Unmixing Under Endmember Variability: A Variational Inference Framework—0
Hyperspectral Unmixing using Iterative, Sparse and Ensambling Approaches for Large Spectral Libraries Applied to Soils and Minerals—0
Hyperspectral Unmixing via Deep Autoencoder Networks for a Generalized Linear-Mixture/Nonlinear-Fluctuation Model—0
Hyperspectral Unmixing via Nonnegative Matrix Factorization with Handcrafted and Learnt Priors—0
Hyperspectral Unmixing with Endmember Variability using Semi-supervised Partial Membership Latent Dirichlet Allocation—0
Hyperspectral Unmixing with Endmember Variability using Partial Membership Latent Dirichlet Allocation—0
Hyperspectral unmixing with spectral variability using adaptive bundles and double sparsity—0
Identifiability-Guaranteed Simplex-Structured Post-Nonlinear Mixture Learning via Autoencoder—0
Identifiability of the Simplex Volume Minimization Criterion for Blind Hyperspectral Unmixing: The No Pure-Pixel Case—0
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