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https://github.com/gokriznastic/HybridSN
A keras based implementation of Hybrid-Spectral-Net as in IEEE GRSL paper "HybridSN: Exploring 3D-2D CNN Feature Hierarchy for Hyperspectral Image Classification".
https://github.com/gokriznastic/HybridSN
3d-cnn hyperspectral-image-classification hyperspectral-imaging remote-sensing
Last synced: 2 days ago
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A keras based implementation of Hybrid-Spectral-Net as in IEEE GRSL paper "HybridSN: Exploring 3D-2D CNN Feature Hierarchy for Hyperspectral Image Classification".
- Host: GitHub
- URL: https://github.com/gokriznastic/HybridSN
- Owner: gokriznastic
- License: mit
- Created: 2019-01-27T18:07:24.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2023-12-23T07:24:11.000Z (7 months ago)
- Last Synced: 2024-03-15T02:39:33.980Z (4 months ago)
- Topics: 3d-cnn, hyperspectral-image-classification, hyperspectral-imaging, remote-sensing
- Language: Jupyter Notebook
- Homepage: https://ieeexplore.ieee.org/document/8736016
- Size: 67.4 MB
- Stars: 283
- Watchers: 6
- Forks: 113
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Lists
- awesome-hyperspectral-image-classification - HybridSN - Spectral-Net as in IEEE GRSL paper "HybridSN: Exploring 3D-2D CNN Feature Hierarchy for Hyperspectral Image Classification". (3 Code / 3.1 Comparison methods of our proposed EMS-GCN methods)
README
# Hybrid-Spectral-Net for Hyperspectral Image Classification.
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/hybridsn-exploring-3d-2d-cnn-feature/hyperspectral-image-classification-on-indian)](https://paperswithcode.com/sota/hyperspectral-image-classification-on-indian?p=hybridsn-exploring-3d-2d-cnn-feature)## PyTorch Implimentation of HybridSN
PyTorch version of the HybridSN is available: https://github.com/Pancakerr/HybridSN
## Description
The HybridSN is spectral-spatial 3D-CNN followed by spatial 2D-CNN. The 3D-CNN facilitates the joint spatial-spectral feature representation from a stack of spectral bands. The 2D-CNN on top of the 3D-CNN further learns more abstract level spatial representation.
## Model
Fig: Proposed HybridSpectralNet (HybridSN) Model with 3D and 2D convolutions for hyperspectral image (HSI) classification.
## Prerequisites
- [Anaconda 2.7](https://www.anaconda.com/download/#linux)
- [Tensorflow 1.3](https://github.com/tensorflow/tensorflow/tree/r1.3)
- [Keras 2.0](https://github.com/fchollet/keras)## Results
### Indian Pines (IP) dataset
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Fig.2 The IN dataset classification result (Overall Accuracy 99.81%) of Hybrid-SN using 30% samples for training. (a) False color image. (b) Ground truth labels. (c) Classification map. (d) Class legend.
### University of Pavia (UP) dataset
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Fig.3 The UP dataset classification result (Overall Accuracy 99.99%) of Hybrid-SN using 30% samples for training. (a) False color image. (b) Ground truth labels. (c) Classification map. (d) Class legend.
### Salinas Scene (SS) dataset
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Fig.4 The UP dataset classification result (Overall Accuracy 100%) of Hybrid-SN using 30% samples for training. (a) False color image. (b) Ground truth labels. (c) Classification map.
#### Detailed results can be found in the [Supplementary Material](supplementary-material.pdf)
## Citation
If you use [HybridSN](https://github.com/gokriznastic/HybridSN) and [A2S2K-ResNet](https://github.com/suvojit-0x55aa/A2S2K-ResNet) and [HSI-Survey](https://github.com/AnkurDeria/HSI-Traditional-to-Deep-Models) code in your research, we would appreciate a citation to both the original paper:
@article{roy2019hybridsn,
title={HybridSN: Exploring 3D-2D CNN Feature Hierarchy for Hyperspectral Image Classification},
author={Roy, Swalpa Kumar and Krishna, Gopal and Dubey, Shiv Ram and Chaudhuri, Bidyut B},
journal={IEEE Geoscience and Remote Sensing Letters},
volume={17},
no.={2},
pp.={277-281},
year={2020}
}
@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}
}
@article{ahmad2021hyperspectral,
title={Hyperspectral Image Classification--Traditional to Deep Models: A Survey for Future Prospects},
author={Muhammad Ahmad, and Sidrah Shabbir, and Swalpa Kumar Roy, and Danfeng Hong, and Xin Wu, and Jing Yao, and Adil Mehmood Khan,
and Manuel Mazzara, and Salvatore Distefano, and Jocelyn Chanussot},
journal={IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
year={2022},
volume={15},
pages={968-999},
doi={10.1109/JSTARS.2021.3133021},
publisher={IEEE}
}
## Acknowledgement
Part of this code is from a implementation of Classification of HSI using CNN by [Konstantinos Fokeas](https://github.com/KonstantinosF/Classification-of-Hyperspectral-Image).
## License
Copyright (c) 2019 Gopal Krishna. Released under the MIT License. See [LICENSE](LICENSE) for details.