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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 51–100 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
Improved Hyperspectral Unmixing With Endmember Variability Parametrized Using an Interpolated Scaling Tensor—0
Inertia-Constrained Pixel-by-Pixel Nonnegative Matrix Factorisation: a Hyperspectral Unmixing Method Dealing with Intra-class Variability—0
Investigation of unsupervised and supervised hyperspectral anomaly detection—0
Low-rank and Sparse NMF for Joint Endmembers' Number Estimation and Blind Unmixing of Hyperspectral Images—0
Matrix cofactorization for joint spatial-spectral unmixing of hyperspectral images—0
Maximum Volume Inscribed Ellipsoid: A New Simplex-Structured Matrix Factorization Framework via Facet Enumeration and Convex Optimization—0
Model-Based Deep Autoencoder Networks for Nonlinear Hyperspectral Unmixing—0
Multilayer Simplex-structured Matrix Factorization for Hyperspectral Unmixing with Endmember Variability—0
Multi-Resolution Beta-Divergence NMF for Blind Spectral Unmixing—0
Multitemporal Latent Dynamical Framework for Hyperspectral Images Unmixing—0
Nonlinear Hyperspectral Unmixing based on Multilinear Mixing Model using Convolutional Autoencoders—0
Nonlinear unmixing of hyperspectral images using a semiparametric model and spatial regularization—0
Nonparametric Detection of Nonlinearly Mixed Pixels and Endmember Estimation in Hyperspectral Images—0
On Hyperspectral Unmixing—0
Online Unmixing of Multitemporal Hyperspectral Images accounting for Spectral Variability—0
Orthogonal Nonnegative Tucker Decomposition—0
Dynamical Hyperspectral Unmixing with Variational Recurrent Neural NetworksCode0
Learning Interpretable Deep Disentangled Neural Networks for Hyperspectral UnmixingCode0
Hyperspectral Unmixing Using a Neural Network AutoencoderCode0
Dual Simplex Volume Maximization for Simplex-Structured Matrix FactorizationCode0
EndNet: Sparse AutoEncoder Network for Endmember Extraction and Hyperspectral UnmixingCode0
Nonlinear hyperspectral unmixing with robust nonnegative matrix factorizationCode0
Deep Spectral Convolution Network for HyperSpectral UnmixingCode0
Low-Rank Tensor Modeling for Hyperspectral Unmixing Accounting for Spectral VariabilityCode0
Regularization Parameter Selection in Minimum Volume Hyperspectral UnmixingCode0
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