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

Mengjiao Qin,Zhenhong Du,Lianjie Qin,Linshu Hu,Feng Zhang,Yi Gao,Renyi Liu

doi:10.3390/rs12121937

Mengjiao Qin, Zhenhong Du + Show 5 more

Open Access

https://doi.org/10.3390/rs12121937

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Journal: Remote Sensing	Publication Date: Jun 15, 2020
Citations: 12	License type: CC BY 4.0

Affiliation: Zhejiang University, Beijing Normal University

Abstract

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.

Highlights

Lakes are dynamic systems that support enormous biodiversity and provide key provisioning and cultural ecosystem services to people around the world [1]
(4) We verify the effectiveness of our method with two data sets, the results demonstrate the superiority of our method in improving the spatial resolution of lake area extraction
By fusing the important gradient information and learning the deep internal features of the given normalized difference water index (NDWI) image, our method can significantly improve the spatial resolution of lakes, which is very important for further analysis and practical applications

Summary

Introduction

Lakes are dynamic systems that support enormous biodiversity and provide key provisioning and cultural ecosystem services to people around the world [1]. Since the changes in lakes, such as expansion and shrinkage are closely related to the effect of climate and human activities [2], lakes can act as a salient indicator of environmental change. In recent decades, accelerated climate warming and rapid economic development have brought about great influence on global lakes. The remote sensing (RS) technique makes long-term and wide-coverage lake monitoring possible. It has been applied to long-term lake evolution [3], lake water storage changes [4], lake level changes [5], etc. Most studies focused on lakes larger than 10 km2 [6,7,8] due to the limitation of the spatial resolution of RS images. As such, generating finer lakes with higher spatial resolution from remote sensing images is of great significance for climate change research

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