Song Xiangyu's repositories
SSIIFD_Hyperspectral-Anomaly-Detection
This is a code set for hyperpsectral anomaly detection including the RX, PTA, CRD, iForest, SSIIFD, MFIFD etc.
A-Bayesian-Ensemble-Algorithm-for-Change-Point-Detection-and-Time-Series-Decomposition
Bayesian Change-Point Detection and Time Series Decomposition
anne-dbscan-demo
Demo of using aNNE similarity for DBSCAN.
anomaly-detection-resources
Anomaly detection related books, papers, videos, and toolboxes
awesome-anomaly-detection
A curated list of awesome anomaly detection resources
awesome-anomaly-detection-in-medical-images
Awesome anomaly detection in medical images
awesome-satellite-imagery-datasets
🛰️ List of satellite image training datasets with annotations for computer vision and deep learning
Bloop
bloop is a fast code search engine written in Rust.
chris-box
A CHRIS/Proba toolbox for SNAP
Clustering
Clustering / Subspace Clustering Algorithms on MATLAB
Codes
isolation kernel
Double-Branch-Dual-Attention-Mechanism-Network
Hyperspectral Image Classification
FG-SuULDA
The code of the paper "Flexible Gabor-based Superpixel-level Unsupervised LDA for Hyperspectral Image Classification".
HSI-classification-based-on-JSaCR
Matlab code for hyperspectral image classification based on JSaCR (IEEE GRSL, 2017)
HSI-SDeCNN
Source code of "A Single Model CNN for Hyperspectral Image Denoising"
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-Image-Super-Resolution-Benchmark
A list of hyperspectral image super-solution resources collected by Junjun Jiang
Hyperspectral_OptimalSpectralClustering
Compute the optimal number of bands essential for dimensionaity reduction
IEEE_TGRS_J-SLoL
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.
iNNE-1
Source code of Isolation‐based anomaly detection
Kernel-based-Clustering-via-Isolation-Distributional-Kernel
The source codes of Kernel-based Clustering via Isolation Distributional Kernel
PTA-HAD
a prior-based tensor approximation (PTA) is proposed for hyperspectral anomaly detection, in which HSI is decomposed into a background tensor and an anomaly tensor. In the background tensor, a low-rank prior is incorporated into spectral dimension by truncated nuclear norm regularization, and a piecewise- smooth prior on spatial dimension can be embedded by a linear total variation-norm regularization. For anomaly tensor, it is unfolded along spectral dimension coupled with spatial group sparse prior that can be represented by l 2,1 -norm regularization.
ptom_c
matlab pfile decrypt to mfile
segment-anything
The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.
sen2like
对sentinel-2和landsat数据光谱一致性处理和融合，提供更高时间分辨率的数据产品，最终目的创建一个虚拟星座
SongXiangyu27
Config files for my GitHub profile.
spectral
Python module for hyperspectral image processing
tensorboardX
tensorboard for pytorch (and chainer, mxnet, numpy, ...)