l7170/MSDesGAT

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5
Rank 2,861,937 (Top 57 %)
Language
Python
Created about 2 years ago
Updated about 2 years ago

l7170/MSDesGAT

l7170

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

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.

LSDM-MoG

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

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