The official repo for [TPAMI'25] "HyperSIGMA: Hyperspectral Intelligence Comprehension Foundation Model"

This repository contains several hyperspectral image analysis algorithms, including unmixing, registration and fusion.

X. Wang, Y. Zhong, L. Zhang, and Y. Xu, “Spatial Group Sparsity Regularized Nonnegative Matrix Factorization for Hyperspectral Unmixing,” IEEE Transactions on Geoscience and Remote Sensing, vol. 55, no. 11, pp.6287-6304, 2017.

Code for the experiments on the Samson Dataset as presented in the paper: Hyperspectral Unmixing Using a Neural Network Autoencoder (Palsson et al. 2018)

Hyperspectral Unmixing via Dual Attention Convolutional Neural Networks | 基于双注意力卷积神经网络的高光谱图像解混

Complete code for linear and non-linear unmixing Hyperspectral images in Python.

Spectral Variability Augmented Sparse Unmixing of Hyperspectral Images & Spatial Purity based Endmember Extraction for Spectral Mixture Analysis

Hyperspectral unmixing using Variational Autoencoders with Dirichlet latent distributions, achieving state-of-the-art performance on endembers and abundances reconstruction.

MultiHU-TD: Multifeature Hyperspectral Unmixing Based on Tensor Decomposition

This repository contains all the codes and some data I used for my undergraduate thesis on Philippine Senate election patterns from 2013-2019 using Hyperspectral Unmixing.

Source code for the paper titled "Towards Weak Signal Analysis in Hyperspectral Data: A Semi-supervised Unmixing Perspective"

Official implementation of our NCC'24 paper titled "Semi‑NMF Regularization‑Based Autoencoder Training for Hyperspectral Unmixing".

Demo of the TGRS paper titled "Efficient Hyperspectral Sparse Regression Unmixing with Multilayers"

Source code for the paper titled "Spatial-Spectral Hyperspectral Endmember Extraction Using a Spatial Energy Prior"

source code for paper titled "Subspace-based Preprocessing Module for Fast Hyperspectral Endmember Selection"

This repository is the official data source for the paper "Advancing blind hyperspectral unmixing in remote sensing: comparing deep-inspired subspace learning methods" by G. Settembre, F. Esposito, N. Del Buono

IEEE TNNLS, 2021