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 1–25 of 113 papers

TitleStatusHype
Entropic Descent Archetypal Analysis for Blind Hyperspectral UnmixingCode1
Differentiable Programming for Hyperspectral Unmixing using a Physics-based Dispersion ModelCode1
Deep Hyperspectral Unmixing using Transformer NetworkCode1
Image Processing and Machine Learning for Hyperspectral Unmixing: An Overview and the HySUPP Python PackageCode1
Endmember-Guided Unmixing Network (EGU-Net): A General Deep Learning Framework for Self-Supervised Hyperspectral UnmixingCode1
Integration of Physics-Based and Data-Driven Models for Hyperspectral Image UnmixingCode1
Hyperspectral Image Super-resolution via Deep Progressive Zero-centric Residual LearningCode1
UnMix-NeRF: Spectral Unmixing Meets Neural Radiance FieldsCode1
A consistent and flexible framework for deep matrix factorizations—0
A Data Dependent Multiscale Model for Hyperspectral Unmixing With Spectral Variability—0
Effective Spectral Unmixing via Robust Representation and Learning-based Sparsity—0
An Elliptic Kernel Unsupervised Autoencoder-Graph Convolutional Network Ensemble Model for Hyperspectral Unmixing—0
A General Framework for Group Sparsity in Hyperspectral Unmixing Using Endmember Bundles—0
A spatial compositional model (SCM) for linear unmixing and endmember uncertainty estimation—0
Transformer based Endmember Fusion with Spatial Context for Hyperspectral Unmixing—0
A Multibranch Convolutional Neural Network for Hyperspectral Unmixing—0
A Low-rank Tensor Regularization Strategy for Hyperspectral Unmixing—0
AE-RED: A Hyperspectral Unmixing Framework Powered by Deep Autoencoder and Regularization by Denoising—0
Distributed Machine Learning with Sparse Heterogeneous Data—0
Deep Diffusion Models and Unsupervised Hyperspectral Unmixing for Realistic Abundance Map Synthesis—0
Correntropy Maximization via ADMM - Application to Robust 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
An Augmented Linear Mixing Model to Address Spectral Variability for Hyperspectral Unmixing—0
A laboratory-created dataset with ground-truth for hyperspectral unmixing evaluation—0
Show:102550
← PrevPage 1 of 5Next →

No leaderboard results yet.