Enlighten-GAN for Super Resolution Reconstruction in Mid-Resolution Remote Sensing Images

Gong, Yuanfu; Liao, Puyun; Zhang, Xiaodong; Zhang, Lifei; Chen, Guanzhou; Zhu, Kun; Tan, Xiaoliang; Lv, Zhiyong

doi:10.3390/rs13061104

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Enlighten-GAN for Super Resolution Reconstruction in Mid-Resolution Remote Sensing Images

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State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), No.129 Luoyu Road, Wuhan 430079, China

Hubei Institute of Land Surveying and Mapping, No.199 Macau Road, Wuhan 430034, China

School of Computer and Engineering, Xi’An University of Technology, No.5 Jin Hua South Road, Xi’an 710048, China

Author to whom correspondence should be addressed.

Academic Editor: Edoardo Pasolli

Remote Sens. 2021, 13(6), 1104; https://doi.org/10.3390/rs13061104

Received: 2 February 2021 / Revised: 8 March 2021 / Accepted: 9 March 2021 / Published: 14 March 2021

(This article belongs to the Special Issue Computational Intelligence and Advanced Learning Techniques in Remote Sensing)

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Abstract

Previously, generative adversarial networks (GAN) have been widely applied on super resolution reconstruction (SRR) methods, which turn low-resolution (LR) images into high-resolution (HR) ones. However, as these methods recover high frequency information with what they observed from the other images, they tend to produce artifacts when processing unfamiliar images. Optical satellite remote sensing images are of a far more complicated scene than natural images. Therefore, applying the previous networks on remote sensing images, especially mid-resolution ones, leads to unstable convergence and thus unpleasing artifacts. In this paper, we propose Enlighten-GAN for SRR tasks on large-size optical mid-resolution remote sensing images. Specifically, we design the enlighten blocks to induce network converging to a reliable point, and bring the Self-Supervised Hierarchical Perceptual Loss to attain performance improvement overpassing the other loss functions. Furthermore, limited by memory, large-scale images need to be cropped into patches to get through the network separately. To merge the reconstructed patches into a whole, we employ the internal inconsistency loss and cropping-and-clipping strategy, to avoid the seam line. Experiment results certify that Enlighten-GAN outperforms the state-of-the-art methods in terms of gradient similarity metric (GSM) on mid-resolution Sentinel-2 remote sensing images. View Full-Text

Keywords: super resolution reconstruction; mid-resolution remote sensing images; generative adversarial network

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MDPI and ACS Style

Gong, Y.; Liao, P.; Zhang, X.; Zhang, L.; Chen, G.; Zhu, K.; Tan, X.; Lv, Z. Enlighten-GAN for Super Resolution Reconstruction in Mid-Resolution Remote Sensing Images. Remote Sens. 2021, 13, 1104. https://doi.org/10.3390/rs13061104

AMA Style

Gong Y, Liao P, Zhang X, Zhang L, Chen G, Zhu K, Tan X, Lv Z. Enlighten-GAN for Super Resolution Reconstruction in Mid-Resolution Remote Sensing Images. Remote Sensing. 2021; 13(6):1104. https://doi.org/10.3390/rs13061104

Chicago/Turabian Style

Gong, Yuanfu, Puyun Liao, Xiaodong Zhang, Lifei Zhang, Guanzhou Chen, Kun Zhu, Xiaoliang Tan, and Zhiyong Lv. 2021. "Enlighten-GAN for Super Resolution Reconstruction in Mid-Resolution Remote Sensing Images" Remote Sensing 13, no. 6: 1104. https://doi.org/10.3390/rs13061104

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Enlighten-GAN for Super Resolution Reconstruction in Mid-Resolution Remote Sensing Images

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