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Open AccessArticle

Achieving Higher Resolution Lake Area from Remote Sensing Images Through an Unsupervised Deep Learning Super-Resolution Method

by 1,2, 1, 1,3,*, 1, 4,5,6, 1,3 and 1,3
1
School of Earth Sciences, Zhejiang University, Hangzhou 310027, China
2
Key Laboratory of Geoscience Big Data and Deep Resource of Zhejiang Province, Zhejiang University, Hangzhou 310027, China
3
Key Laboratory of Geographic Information Science of Zhejiang Province, Zhejiang University, Hangzhou 310028, China
4
Key Laboratory of Environmental Change and Natural Disaster of Ministry of Education, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
5
State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China
6
Academy of Disaster Reduction and Emergency Management, Ministry of Emergency Management and Ministry of Education, Beijing Normal University, Beijing 100875, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(12), 1937; https://doi.org/10.3390/rs12121937
Received: 30 April 2020 / Revised: 3 June 2020 / Accepted: 12 June 2020 / Published: 15 June 2020
Lakes have been identified as an important indicator of climate change and a finer lake area can better reflect the changes. In this paper, we propose an effective unsupervised deep gradient network (UDGN) to generate a higher resolution lake area from remote sensing images. By exploiting the power of deep learning, UDGN models the internal recurrence of information inside the single image and its corresponding gradient map to generate images with higher spatial resolution. The gradient map is derived from the input image to provide important geographical information. Since the training samples are only extracted from the input image, UDGN can adapt to different settings per image. Based on the superior adaptability of the UDGN model, two strategies are proposed for super-resolution (SR) mapping of lakes from multispectral remote sensing images. Finally, Landsat 8 and MODIS (moderate-resolution imaging spectroradiometer) images from two study areas on the Tibetan Plateau in China were used to evaluate the performance of UDGN. Compared with four unsupervised SR methods, UDGN obtained the best SR results as well as lake extraction results in terms of both quantitative and visual aspects. The experiments prove that our approach provides a promising way to break through the limitations of median-low resolution remote sensing images in lake change monitoring, and ultimately support finer lake applications. View Full-Text
Keywords: unsupervised super-resolution; lake; remote sensing; residual network; gradient map unsupervised super-resolution; lake; remote sensing; residual network; gradient map
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MDPI and ACS Style

Qin, M.; Hu, L.; Du, Z.; Gao, Y.; Qin, L.; Zhang, F.; Liu, R. Achieving Higher Resolution Lake Area from Remote Sensing Images Through an Unsupervised Deep Learning Super-Resolution Method. Remote Sens. 2020, 12, 1937. https://doi.org/10.3390/rs12121937

AMA Style

Qin M, Hu L, Du Z, Gao Y, Qin L, Zhang F, Liu R. Achieving Higher Resolution Lake Area from Remote Sensing Images Through an Unsupervised Deep Learning Super-Resolution Method. Remote Sensing. 2020; 12(12):1937. https://doi.org/10.3390/rs12121937

Chicago/Turabian Style

Qin, Mengjiao; Hu, Linshu; Du, Zhenhong; Gao, Yi; Qin, Lianjie; Zhang, Feng; Liu, Renyi. 2020. "Achieving Higher Resolution Lake Area from Remote Sensing Images Through an Unsupervised Deep Learning Super-Resolution Method" Remote Sens. 12, no. 12: 1937. https://doi.org/10.3390/rs12121937

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