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 21–30 of 113 papers

TitleStatusHype
Distributed Machine Learning with Sparse Heterogeneous Data—0
Deep Learning-Based Correction and Unmixing of Hyperspectral Images for Brain Tumor Surgery—0
Deep Nonlinear Hyperspectral Unmixing Using Multi-task Learning—0
An Augmented Linear Mixing Model to Address Spectral Variability for Hyperspectral Unmixing—0
Correntropy Maximization via ADMM - Application to Robust Hyperspectral Unmixing—0
DTU-Net: A Multi-Scale Dilated Transformer Network for Nonlinear Hyperspectral Unmixing—0
A laboratory-created dataset with ground-truth for hyperspectral unmixing evaluation—0
A consistent and flexible framework for deep matrix factorizations—0
Effective Spectral Unmixing via Robust Representation and Learning-based Sparsity—0
Constrained Nonnegative Matrix Factorization for Blind Hyperspectral Unmixing incorporating Endmember Independence—0
Show:102550
← PrevPage 3 of 12Next →

No leaderboard results yet.