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 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

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

🖼️ A tool for efficient processing of spectral images with Python.

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

Independent component analysis for dimensionality reduction of hyperspectral images

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

This is the raw source code of the paper 'Enhancing Hyperspectral Images via Diffusion Model and Group-Autoencoder Super-Resolution Network'

Hyperspectral Pansharpening: Critical Review, Tools and Future Perspectives

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

[AAAI2024] This the implementation of Dual-Window Multiscale Transformer for Hyperspectral Snapshot Compressive Imaging.

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".

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.