A toolbox for spectral compressive imaging reconstruction including MST (CVPR 2022), CST (ECCV 2022), DAUHST (NeurIPS 2022), BiSCI (NeurIPS 2023), HDNet (CVPR 2022), MST++ (CVPRW 2022), etc.

Hyperspectral-Classification Pytorch

The repository contains the implementation of different machine learning techniques such as classification and clustering on Hyperspectral and Satellite Imagery.

Band-Adaptive Spectral-Spatial Feature Learning Deep Neural Network for Hyperspectral Image Classification

Gaussian processes and Bayesian optimization for images and hyperspectral data

Alternately Updated Convolutional Spectral-Spatial Network for Hyperspectral Image Classification(Remote Sensing 2019)

This demo implements FRFE-RX destriping for HSI

Hyperspectral Band Selection using Self-Representation Learning with Sparse 1D-Operational Autoencoder (SRL-SOA)

An R package to simplify working with NEON's hyperspectral imagery

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

Independent component analysis for dimensionality reduction of hyperspectral images

A superpixel-based relational auto-encoder for feature extraction of hyperspectral images

A tool for efficient processing of spectral images with Python.

Spectral Clustering on the Sparse Coefficients of Learned Dictionaries - Published in SIVP

A simple and light CNN-based regression model for soil parameters estimation from hyperspectral images.

The following demo comes for two papers "Spatial-prior generalized fuzziness extreme learning machine autoencoder-based active learning for hyperspectral image classification" and "Multi-layer Extreme Learning Machine-based Autoencoder for Hyperspectral Image Classification".

This toolbox allows the implementation of the Diffusion and Volume maximization-based Image Clustering algorithm for unsupervised hyperspectral image clustering. See "README.md" for more information. Copyright: Sam L. Polk, 2023.

Convolutional neural network for hyperspectral images classification

DCSN: Deep Compressed Sensing Network for Efficient Hyperspectral Data Transmission of Miniaturized Satellite

This is the offical implementation for our paper "An effective cloud detection method for GaoFen-5 images via deep learning"