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Multi-view feature learning for VHR remote sensing image classification

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Abstract

Learning high-level semantic information is important for the task of remote sensing(RS) image scene classification. Due to the great intraclass diversities and the interclass similarities, many researchers have explored the convolutional neural network(CNN) to handle this task recently. However, RS images usually have confusing backgrounds, such as the relevant objects, and features only derived from the whole RS images can not achieve satisfying results. Additionally, the great intraclass diversities also increase the difficulty of recognizing the RS images correctly. To solve the problem, the multi-view feature learning network(MVFLN) is proposed to obtain three domain-specific features for the scene categorization task. FC layers in the VGGNet are replaced by the channel-spatial branch and the other multiple metric branchs. The channel-spatial branch is utilized to localize and learn discriminative regions while the triplet metric branch and the center metric branch are used to enlarge the distance between different classes and reduce the distance of samples belonging to the same class, respectively. In this situation, the proposed MVFLN conducts in a concise way without extra SVM classifiers, achieving better performance. Experiments conducted on the AID, NWPU-RESISC45 and UC Merced datasets evaluate its effectiveness.

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Acknowledgements

This paper was supported by the National Key Research and Development Program of China under grant 2018YFB0505400, the National Natural Science Foundation of China under grants 41822106, the Dawn Scholar of Shanghai Program under grant 18SG22, the State Key Laboratory of Disaster Reduction in Civil Engineering under grant SLDRCE19-B-35, and the Fundamental Research Funds for the Central Universities of China.

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Authors and Affiliations

  1. College of Surveying and Geo-Informatics, Tongji University, Shanghai, 200092, China

    Yiyou Guo & Huan Xie

  2. Department of Automation, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China

    Jinsheng Ji & Qiankun Ye

  3. Workstation of Command Automation 92608 PLA Troops, Shanghai, 200083, China

    Dan Shi

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  1. Yiyou Guo
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Correspondence to Huan Xie.

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Guo, Y., Ji, J., Shi, D. et al. Multi-view feature learning for VHR remote sensing image classification. Multimed Tools Appl 80, 23009–23021 (2021). https://doi.org/10.1007/s11042-020-08713-z

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