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3ddwt-svm-gc

This is an implementation code of paper "Integration of 3-Dimensional Discrete Wavelet Transform and Markov Random Field for Hyperspectral Image Classification"

altoolbox

MATLAB Active Learning Toolbox for Remote Sensing

cnn-al-mrf

This is the code of "Hyperspectral Image Classification with Convolutional Neural Network and Active Learning".

cnn_enhanced_gcn

Q. Liu, L. Xiao, J. Yang and Z. Wei, "CNN-Enhanced Graph Convolutional Network With Pixel- and Superpixel-Level Feature Fusion for Hyperspectral Image Classification," in IEEE Transactions on Geoscience and Remote Sensing, doi: 10.1109/TGRS.2020.3037361.

coursera-ml-andrewng-notes

吴恩达老师的机器学习课程个人笔记

cui

demo_dffn_for_tgrs2018

A deep learning-based method for hyperspectral image classification, which published in IEEE Trans. Geosci. Remote Sens., 2018.

gcn

Implementation of Graph Convolutional Networks in TensorFlow

gcn_hsi_classification

It's a experiment that applying the graph convolution neural network for hyperspectral image classification

gmmdp_for_hic

GMMDP, hyperspectral images classification, multi-view learning

hsi-toolbox

Hyperspectral CNN compression and band selection

hyperspectral-image-classification-using-graph-clustering-methods

hyperspectral_classification_rmg

Matlab code for our JARS18 paper "Spectral and spatial classification of hyperspectral image based on random multi-graphs"

hyperspectralclassification

HSI Classification on the indian pines dataset using various algorithms

ieee_tgrs_gcn

Danfeng Hong, Lianru Gao, Jing Yao, Bing Zhang, Antonio Plaza, Jocelyn Chanussot. Graph Convolutional Networks for Hyperspectral Image Classification, IEEE TGRS, 2021.

lncrna-protein-interaction-prediction

The source codes for our paper submitted to Neurocomputing in 2017. We propose a linear label propagation method (LPLNP method) to predict unknown lncRNA-protein interactions. In this repository, you can find our dataset and code of individual LPLNP, intergrated LPLNP models, and other state-of-the-art methods. Please follow the Guideline.pdf.

mcles

Multi-view Clustering in Latent Embedding Space, AAAI2020

missing-causal-relation

We study risk/benefit tradeoff of missing value imputation in the context of feature selection. We caution against using imputation methods that may yield false positives: features not associated to the target becoming dependent as a result of imputation. We also investigate situations in which imputing missing values may be beneficial to reduce false negatives. We use causal graphs to characterize when structural bias arises and introduce a de-biased version of the t-test.

mssg

Noisy Label Robust Hyperspectral Image Classification

multilayerssl

myproject

Matlab test

nlpir

semi-supervised-learning

group project for semi-supervised learning

sgcmc

The Tensorflow Implementation of paper Self-supervised Graph Convolutional Network For Multi-view Clustering

superpixel-contracted-graph-based-learning-for-hyperspectral-image-classification

Code for the Paper "Superpixel Contracted Graph-Based Learning for Hyperspectral Image Classification"

wsgr

A weighted sparse graph regularization DEMO for hyperspectral image classification.