xiao6-wei6 Goto Github PK
Name: WeiXiao
Type: User
Name: WeiXiao
Type: User
Hyperspectral Image Classification Using Group-Aware Hierarchical Transformer
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.
Official DeiT repository
Official code for Conformer: Local Features Coupling Global Representations for Visual Recognition
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Code (Pytorch) of "Convolution Transformer Mixer for Hyperspectral Image Classification." GRSL-09/2022 Accepted.
🍀 Pytorch implementation of various Attention Mechanisms, MLP, Re-parameter, Convolution, which is helpful to further understand papers.⭐⭐⭐
GroupMixAttention and GroupMixFormer
L. Sun, G. Zhao, Y. Zheng, and Z. Wu. "Spectral–Spatial Feature Tokenization Transformer for Hyperspectral Image Classification," in IEEE TGRS, 2022.
A keras based implementation of Hybrid-Spectral-Net as in IEEE GRSL paper "HybridSN: Exploring 3D-2D CNN Feature Hierarchy for Hyperspectral Image Classification".
Danfeng Hong, Zhu Han, Jing Yao, Lianru Gao, Bing Zhang, Antonio Plaza, Jocelyn Chanussot. Spectralformer: Rethinking hyperspectral image classification with transformers, IEEE Transactions on Geoscience and Remote Sensing (TGRS), 2021
M3FuNet--IEEE TGRS2024
[INFFUS] Official implementation for "Multiscale spatial–spectral transformer network for hyperspectral and multispectral image fusion."
The official implementation of S$^{3}$ANet
1. PyTorch Implementation of SSTNs for hyperspectral image classifications from the IEEE T-GRS paper "Spectral-Spatial Transformer Network for Hyperspectral Image Classification: A FAS Framework." 2. SSRN is based on Patch, the paper link is provided, see the end of web
Bayesian Adaptive Superpixel Segmentation (ICCV 2019)
FPGA: Fast Patch-Free Global Learning Framework for Fully End-to-End Hyperspectral Image Classification (TGRS 2020) https://ieeexplore.ieee.org/document/9007624
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