HSI-GCN: hyperspectral image classification algorithm based on Gabor convolutional networks
ISSN: 1708-5284
Article publication date: 31 May 2021
Issue publication date: 29 July 2021
Abstract
Purpose
Hyperspectral imaging (HSI) systems are becoming potent technologies for computer vision tasks due to the rich information they uncover, where each substance exhibits a distinct spectral distribution. Although the high spectral dimensionality of the data empowers feature learning, the joint spatial–spectral features have not been well explored yet. Gabor convolutional networks (GCNs) incorporate Gabor filters into a deep convolutional neural network (CNN) to extract discriminative features of different orientations and frequencies. To the best if the authors’ knowledge, this paper introduces the exploitation of GCNs for hyperspectral image classification (HSI-GCN) for the first time. HSI-GCN is able to extract deep joint spatial–spectral features more rapidly and accurately despite the shortage of training samples. The authors thoroughly evaluate the effectiveness of used method on different hyperspectral data sets, where promising results and high classification accuracy have been achieved compared to the previously proposed CNN-based and Gabor-based methods.
Design/methodology/approach
The authors have implemented the new algorithm of Gabor convolution network on the hyperspectral images for classification purposes.
Findings
Implementing the new GCN has shown unexpectable results with an excellent classification accuracy.
Originality/value
To the best of the authors’ knowledge, this work is the first one that implements this approach.
Keywords
Acknowledgements
This work was partially supported by the National Natural Science Foundation of China under grant #91648205, the Aeronautical Foundation of China under grant #20185851022 and Shenzhen Science and Technology Program (No. KQTD2016112515134654).
Citation
Youcef Moudjib, H., Haibin, D., Zhang, B. and Ahmed Ghaleb, M.S. (2021), "HSI-GCN: hyperspectral image classification algorithm based on Gabor convolutional networks", World Journal of Engineering, Vol. 18 No. 4, pp. 590-595. https://doi.org/10.1108/WJE-09-2020-0460
Publisher
:Emerald Publishing Limited
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