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RSID: A Remote Sensing Image Dehazing Network

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Pattern Recognition and Computer Vision (PRCV 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14430))

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Abstract

Hazy images often lead to problems such as loss of image details and dull colors, which significantly affects the information extraction of remote sensing images, so it is necessary to research image dehazing. In the field of remote sensing, remote sensing images are characterized by large-size and rich information, so the processing of remote sensing images often has the problems of GPU memory overflow and difficult removal of non-uniform haze. For remote sensing image characteristics, an efficient and lightweight end-to-end dehazing method is proposed in this paper. We use the FA attention combined with smoothed dilated convolution instead as the main structure of the encoder, which can achieve imbalanced handling of hazy images with different levels of opacity while reducing parameter count. Channel weight fusion self-attention is added in the decoder part to realize the automatic learning and pixel-level processing of different receptive field features We tested the proposed method on both public datasets RESIDE and real large-size hazy remote sensing images. The proposed method achieved satisfactory results in our experiments, which proves the effectiveness of the proposed method.

Supported by National Natural Science Foundation of China (61975028) and Jiao Xu.

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Authors and Affiliations

  1. Northeast Forestry University, No. 26, Hexing Road, Harbin, 150036, Heilongjiang, China

    Yuan Li & Yafeng Zhao

Authors
  1. Yuan Li
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  2. Yafeng Zhao
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Corresponding author

Correspondence to Yafeng Zhao .

Editor information

Editors and Affiliations

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

    Qingshan Liu

  2. Xiamen University, Xiamen, China

    Hanzi Wang

  3. Beijing University of Posts and Telecommunications, Beijing, China

    Zhanyu Ma

  4. Sun Yat-sen University, Guangzhou, China

    Weishi Zheng

  5. Peking University, Beijing, China

    Hongbin Zha

  6. Chinese Academy of Sciences, Beijing, China

    Xilin Chen

  7. Chinese Academy of Sciences, Beijing, China

    Liang Wang

  8. Xiamen University, Xiamen, China

    Rongrong Ji

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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Li, Y., Zhao, Y. (2024). RSID: A Remote Sensing Image Dehazing Network. In: Liu, Q., et al. Pattern Recognition and Computer Vision. PRCV 2023. Lecture Notes in Computer Science, vol 14430. Springer, Singapore. https://doi.org/10.1007/978-981-99-8537-1_1

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  • DOI: https://doi.org/10.1007/978-981-99-8537-1_1

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-8536-4

  • Online ISBN: 978-981-99-8537-1

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