SOTAVerified

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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 76–100 of 113 papers

TitleStatusHype
Improving Autoencoder Training Performance for Hyperspectral Unmixing with Network Reinitialisation—0
Structured Sparse Method for Hyperspectral Unmixing—0
Successive Projection Algorithm Robust to Outliers—0
Superpixel Based Graph Laplacian Regularization for Sparse Hyperspectral Unmixing—0
Tech Report: A Fast Multiscale Spatial Regularization for Sparse Hyperspectral Unmixing—0
Theoretical and Practical Progress in Hyperspectral Pixel Unmixing with Large Spectral Libraries from a Sparse Perspective—0
Unrolling Plug-and-Play Network for Hyperspectral Unmixing—0
A consistent and flexible framework for deep matrix factorizations—0
Variable-Wise Diagonal Preconditioning for Primal-Dual Splitting: Design and Applications—0
Adaptive Multi-Order Graph Regularized NMF with Dual Sparsity for Hyperspectral Unmixing—0
A Data Dependent Multiscale Model for Hyperspectral Unmixing With Spectral Variability—0
A Dual Symmetric Gauss-Seidel Alternating Direction Method of Multipliers for Hyperspectral Sparse Unmixing—0
AE-RED: A Hyperspectral Unmixing Framework Powered by Deep Autoencoder and Regularization by Denoising—0
A General Framework for Group Sparsity in Hyperspectral Unmixing Using Endmember Bundles—0
A graph Laplacian regularization for hyperspectral data unmixing—0
A laboratory-created dataset with ground-truth for hyperspectral unmixing evaluation—0
A Low-rank Tensor Regularization Strategy for Hyperspectral Unmixing—0
A Multibranch Convolutional Neural Network for Hyperspectral Unmixing—0
An Augmented Linear Mixing Model to Address Spectral Variability for Hyperspectral Unmixing—0
An Elliptic Kernel Unsupervised Autoencoder-Graph Convolutional Network Ensemble Model for Hyperspectral Unmixing—0
A spatial compositional model (SCM) for linear unmixing and endmember uncertainty estimation—0
Constrained Nonnegative Matrix Factorization for Blind Hyperspectral Unmixing incorporating Endmember Independence—0
Correntropy Maximization via ADMM - Application to Robust Hyperspectral Unmixing—0
Distributed Machine Learning with Sparse Heterogeneous Data—0
Deep Diffusion Models and Unsupervised Hyperspectral Unmixing for Realistic Abundance Map Synthesis—0
Show:102550
← PrevPage 4 of 5Next →

No leaderboard results yet.