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A Study of CNN-based Super-Resolution Method for Remote Sensing Image
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원격 탐사 영상을 활용한 CNN 기반의 초해상화 기법 연구

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Type
Academic journal
Author
Journal
The Korean Society Of Remote Sensing 대한원격탐사학회지 대한원격탐사학회지 제36권 제3호 KCI Accredited Journals
Published
2020.1
Pages
449 - 460 (12page)

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