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Feature Difference Enhancement Fusion for Remote Sensing Image Change Detection

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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

Remote sensing image change detection identifies pixel-wise differences between bitemporal images. It is of great significance for geographic monitoring. However, existing approaches still lack efficiency when dealing with the change features. The most general manner is to introduce attention mechanisms in different time streams to strengthen the features and then superimpose them together to complete the fusion of the features. These methods can not effectively excavate and apply the relationship between different temporal features. To alleviate this problem, we introduce a feature difference enhancement fusion module based on pixel position offset in the time dimension (time-position offset). We will learn the offset of the pixel changes in the corresponding areas between the bitemporal features, which will be used to guide the enhancement of the difference between the change-related areas and the change-irrelated areas in a single feature map. Meanwhile, we propose a general and straightforward change detection framework composed of the basic ResNet18 as the encoder and a simple MLP structure as the decoder, instead of the complex structures like UNet or FPN. Extensive experiments on three datasets, including LEVIR-CD, LEVIR-CD+, and S2Looking datasets, demonstrate the effectiveness of our method.

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Acknowledgments

This work was supported by the pre-research project of the Equipment Development Department of the Central Military Commission (No. 31514020205).

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Authors and Affiliations

  1. Nanjing University of Science and Technology, Nanjing, 210094, China

    Renjie Hu, Gensheng Pei, Pai Peng, Tao Chen & Yazhou Yao

Authors
  1. Renjie Hu
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  2. Gensheng Pei
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  3. Pai Peng
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  4. Tao Chen
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Corresponding author

Correspondence to Yazhou Yao .

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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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Hu, R., Pei, G., Peng, P., Chen, T., Yao, Y. (2022). Feature Difference Enhancement Fusion for Remote Sensing Image Change Detection. 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_40

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  • DOI: https://doi.org/10.1007/978-3-031-18913-5_40

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