Hybrid-Spectral-Net for Hyperspectral Image Classification.

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
• Tensorflow 1.3
• Keras 2.0

Results

Indian Pines (IP) dataset

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

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

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

Citation

If you use HybridSN and A2S2K-ResNet and HSI-Survey 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.

License

Copyright (c) 2019 Gopal Krishna. Released under the MIT License. See LICENSE for details.