A Study of CNN-based Super-Resolution Method for Remote Sensing Image

원격 탐사 영상을 활용한 CNN 기반의 초해상화 기법 연구

  • Choi, Yeonju (Artificial Intelligence Research Section, Korea Aerospace Research Institute) ;
  • Kim, Minsik (Naraspace Technology) ;
  • Kim, Yongwoo (Department of System Semiconductor Engineering, Sangmyung University) ;
  • Han, Sanghyuck (Artificial Intelligence Research Section, Korea Aerospace Research Institute)
  • 최연주 (한국항공우주연구원 인공지능연구실) ;
  • 김민식 (나라스페이스 테크놀로지) ;
  • 김용우 (상명대학교 시스템반도체공학과) ;
  • 한상혁 (한국항공우주연구원 인공지능연구실)
  • Received : 2020.06.04
  • Accepted : 2020.06.16
  • Published : 2020.06.30


Super-resolution is a technique used to reconstruct an image with low-resolution into that of high-resolution. Recently, deep-learning based super resolution has become the mainstream, and applications of these methods are widely used in the remote sensing field. In this paper, we propose a super-resolution method based on the deep back-projection network model to improve the satellite image resolution by the factor of four. In the process, we customized the loss function with the edge loss to result in a more detailed feature of the boundary of each object and to improve the stability of the model training using generative adversarial network based on Wasserstein distance loss. Also, we have applied the detail preserving image down-scaling method to enhance the naturalness of the training output. Finally, by including the modified-residual learning with a panchromatic feature in the final step of the training process. Our proposed method is able to reconstruct fine features and high frequency information. Comparing the results of our method with that of the others, we propose that the super-resolution method improves the sharpness and the clarity of WorldView-3 and KOMPSAT-2 images.


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