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Cascade Scale-Aware Distillation Network for Lightweight Remote Sensing Image Super-Resolution

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Pattern Recognition and Computer Vision (PRCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13537))

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Abstract

Recently, convolution neural network based methods have dominated the remote sensing image super-resolution (RSISR). However, most of them own complex network structures and a large number of network parameters, which is not friendly to computational resources limited scenarios. Besides, scale variations of objects in the remote sensing image are still challenging for most methods to generate high-quality super-resolution results. To this end, we propose a scale-aware group convolution (SGC) for RSISR. Specifically, each SGC firstly uses group convolutions with different dilation rates for extracting multi-scale features. Then, a scale-aware feature guidance approach and enhancement approach are leveraged to enhance the representation ability of different scale features. Based on SGC, a cascaded scale-aware distillation network (CSDN) is designed, which is composed of multiple SGC based cascade scale-aware distillation blocks (CSDBs). The output of each CSDB will be fused via the backward feature fusion module for final image super-resolution reconstruction. Extensive experiments are performed on the commonly-used UC Merced dataset. Quantitative and qualitative experiment results demonstrate the effectiveness of our method.

This work is supported by the Natural Science Foundation of Hebei Province (F2019201451).

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Authors and Affiliations

  1. Beijing Laboratory of Intelligent Information Technology, School of Computer Science and Technology, Beijing Institute of Technology, Beijing, 100081, China

    Haowei Ji, Huijun Di & Shunzhou Wang

  2. School of Cyber Security and Computer, Hebei University, Baoding, 071000, China

    Qingxuan Shi

Authors
  1. Haowei Ji
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  2. Huijun Di
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  3. Shunzhou Wang
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  4. Qingxuan Shi
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Corresponding author

Correspondence to Huijun Di .

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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Ji, H., Di, H., Wang, S., Shi, Q. (2022). Cascade Scale-Aware Distillation Network for Lightweight Remote Sensing Image Super-Resolution. In: Yu, S., et al. Pattern Recognition and Computer Vision. PRCV 2022. Lecture Notes in Computer Science, vol 13537. Springer, Cham. https://doi.org/10.1007/978-3-031-18916-6_23

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  • DOI: https://doi.org/10.1007/978-3-031-18916-6_23

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-031-18916-6

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