Song Xiangyu's Projects
Source code of "A Single Model CNN for Hyperspectral Image Denoising"
This is a hypersepctral image compression matlab toolbox.
A Repo collection of the algorithms described in the appendix of Chein-I Chang's book: Hyperspectral Data Processing: Algorithm Design and Analysis
Hyperspectral Image Denoising via Subspace-Based Nonlocal Low-Rank and Sparse Factorization
Python code of Hilbert-Schmidt Independence Criterion
This Toolbox includes Hyperspectral Feature Extraction Techniques including Unsupervised, Supervised, and Deep Feature Extraction
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
This demo implements FRFE-RX destriping for HSI
This is my Graduate Project on hyperspectral image classification.
A list of hyperspectral image denoising resources collected by Yongsen Zhao and Junjun Jiang.
A list of hyperspectral image super-solution resources collected by Junjun Jiang
Hyperspectral image Target Detection based on Sparse Representation
Compute the optimal number of bands essential for dimensionaity reduction
HyperSpectral Matlab Toolbox forked from Sourceforge
Lianru Gao, Danfeng Hong, Jing Yao, Bing Zhang, Paolo Gamba, Jocelyn Chanussot. Spectral Superresolution of Multispectral Imagery with Joint Sparse and Low-Rank Learning, IEEE TGRS, 2020.
iForest anomaly detection codes (Matlab Version)
In this repository you will find an implementation in matlab to pass an RGB image to HSI and from HSI to RGB.
Source code of Isolation‐based anomaly detection
Anomaly Detection based on Isolation Forest
(Python, R, C++) Extended Isolation Forest, SCiForest, Fair-Cut Forest, with some additions (outlier detection + NA imputation + similarity)
Outlier/anomaly detection for Ruby using Isolation Forest
Torch code for our CVPR 2016 paper "Joint Unsupervised LEarning of Deep Representations and Image Clusters"
The source codes of Kernel-based Clustering via Isolation Distributional Kernel
KPCA for dimensionality reduction, feature extraction , fault detection, and fault diagnosis
Library for Kernel Online Learning
Efficient, transparent deep learning in hundreds of lines of code.
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