l7170/LSDM-MoG

Stars
16
Rank 1,311,288 (Top 26 %)
Language
Created over 5 years ago
Updated over 5 years ago

l7170/LSDM-MoG

l7170

There are no reviews yet. Be the first to send feedback to the community and the maintainers!

Low-Rank and Sparse Decomposition with Mixture-of-Gaussian for Hyperspectral Anomaly Detection

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.

MSDesGAT

Multi-Scale Dense Graph Attention net- work (MSDesGAT) for hyperspectral classification

Segment_tree4SemiSupervised-Classification

Semi-Supervised Classfication for Hyperspectral image based on Segment-tree and SELF.

NNLS-FCLS-GPU

GPU Implementation of Large-Scale Abundance Estimation with Constraint Linear Spectral Mixture Model for Hyperspectral Unmixing

CSTSUNet

A Cross Swin Transformer Based Siamese U-Shape Network for Change Detection in Remote Sensing Images