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Category-Oriented Adversarial Data Augmentation via Statistic Similarity for Satellite Images

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Pattern Recognition and Computer Vision (PRCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13536))

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Abstract

Deep learning is one of the essential technologies for remote sensing tasks, which heavily depends on the quantity of training data. However, it is difficult to obtain or label the remotely sensed images in their non-cooperative imaging mode. Data augmentation is a viable solution to this issue, but most of the current data augmentation methods are task specific or dataset specific, which are not as applicable as a generalized solution for the remotely sensed images. In this paper, we propose a category-oriented adversarial data augmentation method using statistic similarity cross categories, which formulates the common appearance-based statistic factors in the object detection into a combination index, to depict the statistic similarity between different categories and to generate new adversarial samples between similar categories with more reliable physical significance. Experimental results demonstrated that, taking the most advanced RT method as a baseline, the total mAP can be increased by 2.0% on the DOTA dataset for the object detection task by using our proposed method.

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Acknowledgement

This work was supported in part by the National Nature Science Foundation (41971294), China Postdoctoral Science Foundation (2020M680560) and Cross-Media Intelligent Technology Project of BNRist (BNR2019TD01022) of China.

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Authors and Affiliations

  1. Department of Electronic Engineering, Tsinghua University, Beijing, China

    Huan Zhang, Wei Leng, Xiaolin Han & Weidong Sun

  2. Institute for Ocean Engineering, Tsinghua University, Beijing, China

    Huan Zhang, Wei Leng, Xiaolin Han & Weidong Sun

Authors
  1. Huan Zhang
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  2. Wei Leng
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  3. Xiaolin Han
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  4. Weidong Sun
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Corresponding author

Correspondence to Weidong Sun .

Editor information

Editors and Affiliations

  1. Southern University of Science and Technology, Shenzhen, China

    Shiqi Yu

  2. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Zhaoxiang Zhang

  3. Hong Kong Baptist University, Hong Kong, China

    Pong C. Yuen

  4. Northwestern Polytechnical University, Xi'an, China

    Junwei Han

  5. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Tieniu Tan

  6. Hong Kong Baptist University, Hong Kong, China

    Yike Guo

  7. Sun Yat-sen University, Guangzhou, China

    Jianhuang Lai

  8. Southern University of Science and Technology, Shenzhen, China

    Jianguo Zhang

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Zhang, H., Leng, W., Han, X., Sun, W. (2022). Category-Oriented Adversarial Data Augmentation via Statistic Similarity for Satellite Images. In: Yu, S., et al. Pattern Recognition and Computer Vision. PRCV 2022. Lecture Notes in Computer Science, vol 13536. Springer, Cham. https://doi.org/10.1007/978-3-031-18913-5_37

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  • DOI: https://doi.org/10.1007/978-3-031-18913-5_37

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-18912-8

  • Online ISBN: 978-3-031-18913-5

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