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hierarchicalmatrixcompletion

Code for the Hierarchical Matrix completion for Inverse Problems

hrmc

High-rank matrix completion for gene prioritisation

hsi-classification

Hyperspectral image classification with varies image processing and machine learning methods.

hsi-cnn

Classification of Hyperspectral Remote Sensing Images Based on Convolutional Neural Network

hsi-datasets

Hyperspectral Remote Sensing Scenes

hsi-denoiser-glf

An HSI denoiser: Global Local Factorization (GLF)

hsi-experiment

algorithm for HSI-experiment

hsi-sdecnn

Source code of "A Single Model CNN for Hyperspectral Image Denoising"

hsiclassification

Supervised classification of hyperspectral image(HSI)

hsic_toolbox

Matlab Hyperspectral Image Classification Toolbox. This toolbox included object-oriented data model of hyperspectral image for classification purpose. Moreover, some state-of-art classification methods will be included.

hsi_classification

Hyperspectral image classification

hsi_nonlinear_unmixing_lr-ntf

A hyperspectral nonlinear unmixing method LR-NTF

hsr_software

hypergraph-learning-based-discriminative-band-selection

For hyperspectral images (HSIs), it is a challenging task to select discriminative bands due to the lack of labeled samples and complex noise. In this article, we present a novel local-view-assisted discriminative band selection method with hypergraph autolearning (LvaHAl) to solve these problems from both local and global perspectives. Specifically, the whole band space is first randomly divided into several subspaces (LVs) of different dimensions, where each LV denotes a set of lowdimensional representations of training samples consisting of bands associated with it. Then, for different LVs, a robust hinge loss function for isolated pixels regularized by the row-sparsity is adopted to measure the importance of the corresponding bands. In order to simultaneously reduce the bias of LVs and encode the complementary information between them, samples from all LVs are further projected into the label space. Subsequently, a hypergraph model that automatically learns the hyperedge weights is presented. In this way, the local manifold structure of these projections can be preserved, ensuring that samples of the same class have a small distance. Finally, a consensus matrix is used to integrate the importance of bands corresponding to different LVs, resulting in the optimal selection of expected bands from a global perspective. The classification experiments on three HSI data sets show that our method is competitive with other comparison methods

hyperspectral

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

hyperspectral-classification-cnn

paper source code

hyperspectral-classification-with-svm

This repo contains an illustration of the use of kernel support vector machine for hyperspectral image classifiication as well as a comparison with least sqaure methods.

hyperspectral-classifier

Hyperspectral Classification

hyperspectral-data-for-visualzation

The data are accquired from then internet

hyperspectral-data-processing

Spectral data processing, including preprocessing, feature extraction, sample division, modeling, optimization algorithm.

hyperspectral-image-classification

Hyperspectral Image (HSI) classification with matlab

hyperspectral-image-classification-1

Multi-class classification with SVMs for hyperspectral images

hyperspectral-image-classification-2

hyperspectral-image-classification-using-reduced-kpca

hyperspectral-image-classification-via-fusing-correlation-coefficient-and-joint-sparse-representatio

the code of ''Hyperspectral Image Classification via Fusing Correlation Coefficient and Joint Sparse Representation''

hyperspectral-image-denoising-via-sparse-representation-and-low-rank-constraint

Zhao Y Q, Yang J. Hyperspectral image denoising via sparse representation and low-rank constraint[J]. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(1): 296-308.

hyperspectral-image-recovery-via-hybrid-regularization

Data and MATLAB code for the simulations of [R. Arablouei and F. de Hoog, “Hyperspectral image recovery via hybrid regularization,” IEEE Transactions on Image Processing, 2016.]

hyperspectral-image-restoration-via-total-variation-regularized-low-rank-tensor-decomposition

code of Hyperspectral Image Restoration via Total Variation Regularized Low-rank Tensor Decomposition

hyperspectral-image-visualization

Matlab Code of Decolorization-based hyperspectral image visualization, TGRS, 2018

hyperspectral-image-visualization-based-on-mpcnn

Matlab Code for Multichannel Pulse-Coupled Neural Network-Based Hyperspectral Image Visualization, 2020, TGRS