There are 2 repositories under hyperspectral-image topic.
This repository contains several hyperspectral image analysis algorithms, including unmixing, registration and fusion.
HSCNN+: Advanced CNN-Based Hyperspectral Recovery from RGB Images in CVPRW 2018 (Winner of NTIRE Challenge)
This repository is the official code for DBIN (ICCV 2019) and EDBIN (TNNLS 2021)
Hyperspectral Image Classification using Naive Bayes, Minimum Eucleidian Distance and KNN in Matlab
Implementation of CNN-Enhanced Graph Attention Network for Hyperspectral Image Super-Resolution Using Non-Local Self-Similarity (CEGATSR) in Pytorch.
Project for Machine Learning and Physical Applications Class - Hyperspectral image classification using SVM, and CNN with layer pruning and layer compression.
The GUI will preprocess the hyperspectral images i.e. removes the bands having negative values. The image format allowed is ".ENVI".
Landcover classification using the fusion of HSI and LiDAR data.
MLNMF: Multilayer Nonnegative Matrix Factorization
A Pytorch implementation of "An Efficient Unfolding Network with Disentangled Spatial-Spectral Representation for Hyperspectral Image Super-Resolution"
Abraia-Multiple image analysis toolbox
the official implementation of paper Attention-Based Second-Order Pooling Network for Hyperspectral Image Classification (A-SPN).
Codes for the paper "Deep sparse and low-rank for HSI denoising" in Proceeding of IGARSS 2022, Kuala Lumpur.
Compression and Reinforced Variation (CRV) Method
This repository contains the python (2.7) code running the detection within astronomical hyperspectral images. It is associated with the paper : "Extended faint source detection in astronomical hyperspectral images" published by Courbot, J.-B. et al in Signal Processing 135 (2017) 274–283.
MultiHU-TD: Multifeature Hyperspectral Unmixing Based on Tensor Decomposition
Hyperspectral Image Denoising using Attention and Adjacent Features Extraction Hybrid Dense Network
MATLAB based Information Theory Project on Information Theoretical Approach to Spectral variability