Hybrid-Spectral-Net for Hyperspectral Image Classification.

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.4 The IN dataset classification result (Overall Accuracy 99.86%) of Hybrid-SN using 30% samples for training. (a) False color image. (b) Ground truth labels. (c) Classification map.

University of Pavia (UP) dataset

Fig.5 The UP dataset classification result (Overall Accuracy 99.98%) of Hybrid-SN using 30% samples for training. (a) False color image. (b) Ground truth labels. (c) Classification map.

Salinas (SA) dataset

Fig.5 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 this code in your research, we would appreciate a citation to 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},
        year={2019}
        }

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.