Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/suvojit-0x55aa/A2S2K-ResNet
A2S2K-ResNet: Attention-Based Adaptive Spectral-Spatial Kernel ResNet for Hyperspectral Image Classification
https://github.com/suvojit-0x55aa/A2S2K-ResNet
3d-cnn cnn colab colab-notebook colaboratory hyperspectral hyperspectral-image-classification hyperspectral-imaging image-classification remote-sensing residual-networks
Last synced: about 1 month ago
JSON representation
A2S2K-ResNet: Attention-Based Adaptive Spectral-Spatial Kernel ResNet for Hyperspectral Image Classification
- Host: GitHub
- URL: https://github.com/suvojit-0x55aa/A2S2K-ResNet
- Owner: suvojit-0x55aa
- Created: 2020-05-31T14:31:21.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2022-11-25T15:25:36.000Z (almost 2 years ago)
- Last Synced: 2024-07-05T14:29:47.848Z (2 months ago)
- Topics: 3d-cnn, cnn, colab, colab-notebook, colaboratory, hyperspectral, hyperspectral-image-classification, hyperspectral-imaging, image-classification, remote-sensing, residual-networks
- Language: Python
- Homepage:
- Size: 5.26 MB
- Stars: 187
- Watchers: 6
- Forks: 37
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- awesome-hyperspectral-image-classification - A2S2K-ResNet - Based Adaptive Spectral-Spatial Kernel ResNet for Hyperspectral Image Classification. (3 Code / 3.1 Comparison methods of our proposed EMS-GCN methods)
README
[](https://paperswithcode.com/sota/hyperspectral-image-classification-on-kennedy?p=attention-based-adaptive-spectral-spatial)
[](https://paperswithcode.com/sota/hyperspectral-image-classification-on-pavia?p=attention-based-adaptive-spectral-spatial)
[](https://paperswithcode.com/sota/hyperspectral-image-classification-on-indian?p=attention-based-adaptive-spectral-spatial)
# Attention-Based Adaptive Spectral-Spatial Kernel ResNet for Hyperspectral Image Classification
This repository is the official implementation of [Attention-Based Adaptive Spectral-Spatial Kernel ResNet for Hyperspectral Image Classification](https://ieeexplore.ieee.org/document/9306920).
[](https://colab.research.google.com/drive/1x2CYfaUXNjX4yDMLCvoVFqAMZXZwwVgS)
>📋 Abstract:
Hyperspectral images (HSIs) provide rich spectral-spatial information with stacked hundreds of contiguous narrowbands. Due to the existence of noise and band correlation, the selection of informative spectral-spatial kernel features poses a challenge. This is often addressed by using convolutional neural networks (CNNs) with receptive field (RF) having fixed sizes. However, these solutions cannot enable neurons to effectively adjust RF sizes and cross-channel dependencies when forward and backward propagations are used to optimize the network. In this article, we present an attention-based adaptive spectral-spatial kernel improved residual network (A²S²K-ResNet) with spectral attention to capture discriminative spectral-spatial features for HSI classification in an end-to-end training fashion. In particular, the proposed network learns selective 3-D convolutional kernels to jointly extract spectral-spatial features using improved 3-D ResBlocks and adopts an efficient feature recalibration (EFR) mechanism to boost the classification performance. Extensive experiments are performed on three well-known hyperspectral data sets, i.e., IP, KSC, and UP, and the proposed A²S²K-ResNet can provide better classification results in terms of overall accuracy (OA), average accuracy (AA), and Kappa compared with the existing methods investigated.
## Requirements
To install requirements:
```setup
conda env create -f environment.yml
```
To download the dataset and setup the folders, run:
```
bash setup_script.sh
```
## Training
To train the model(s) in the paper, run this command in the A2S2KResNet folder:
```train
python A2S2KResNet.py -d -e 200 -i 3 -p 3 -vs 0.9 -o adam
```
## Results
Our model achieves the following performance on 10% of datasets:
### [India Pines](http://www.ehu.eus/ccwintco/uploads/6/67/Indian_pines_corrected.mat) dataset
| Model name | OA |
| ------------------ |---------------- |
| A2S2K-ResNet | 98.66 ± 0.004 % |
### [Kennedy Space Center](http://www.ehu.es/ccwintco/uploads/2/26/KSC.mat) dataset
| Model name | OA |
| ------------------ |---------------- |
| A2S2K-ResNet | 99.34 ± 0.001 % |
### [University of Pavia](http://www.ehu.eus/ccwintco/uploads/e/ee/PaviaU.mat) dataset
| Model name | OA |
| ------------------ |---------------- |
| A2S2K-ResNet | 99.85 ± 0.001 % |
For deatiled results refer to Table IV-VII of our paper.
## Citation
If you use A2S2K-ResNet code in your research, we would appreciate a citation to the original paper:
```
@article{roy2020attention,
title={Attention-based adaptive spectral-spatial kernel resnet for hyperspectral image classification},
author={Swalpa Kumar Roy, and Suvojit Manna, and Tiecheng Song, and Lorenzo Bruzzone},
journal={IEEE Transactions on Geoscience and Remote Sensing},
volume={59},
no.={9},
pp.={7831-7843},
year={2021},
publisher={IEEE}
}
```