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

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

Independent component analysis for dimensionality reduction of hyperspectral images

A tool for efficient processing of spectral images with Python.

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

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

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.

A complete solution to utilise both spatial and spectral information in the classification process is provided by the integration of deep CNNs with PCA for feature extraction and dimensionality reduction.

Convolutional neural network for hyperspectral images classification

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