Version 1
: Received: 6 April 2021 / Approved: 29 July 2021 / Online: 29 July 2021 (11:08:09 CEST)
How to cite:
Zhang, L.; Dong, R.; Yuan, S.; Li, W.; Zheng, J.; Fu, H. Making Low-Resolution Satellite Images Reborn: A Deep Learning Approach for Super-Resolution Building Extraction. Preprints2021, 2021040209. https://doi.org/10.20944/preprints202104.0209.v1
Zhang, L.; Dong, R.; Yuan, S.; Li, W.; Zheng, J.; Fu, H. Making Low-Resolution Satellite Images Reborn: A Deep Learning Approach for Super-Resolution Building Extraction. Preprints 2021, 2021040209. https://doi.org/10.20944/preprints202104.0209.v1
Zhang, L.; Dong, R.; Yuan, S.; Li, W.; Zheng, J.; Fu, H. Making Low-Resolution Satellite Images Reborn: A Deep Learning Approach for Super-Resolution Building Extraction. Preprints2021, 2021040209. https://doi.org/10.20944/preprints202104.0209.v1
APA Style
Zhang, L., Dong, R., Yuan, S., Li, W., Zheng, J., & Fu, H. (2021). Making Low-Resolution Satellite Images Reborn: A Deep Learning Approach for Super-Resolution Building Extraction. Preprints. https://doi.org/10.20944/preprints202104.0209.v1
Chicago/Turabian Style
Zhang, L., Juepeng Zheng and Haohuan Fu. 2021 "Making Low-Resolution Satellite Images Reborn: A Deep Learning Approach for Super-Resolution Building Extraction" Preprints. https://doi.org/10.20944/preprints202104.0209.v1
Abstract
Existing methods for building extraction from remotely sensed images strongly rely on aerial or satellite-based images with very high resolution, which are usually limited by spatiotemporally accessibility and cost. In contrast, relatively low-resolution images have better spatial and temporal availability but cannot directly contribute to fine- and/or high-resolution building extraction. In this paper, based on image super-resolution and segmentation techniques, we propose a two-stage framework (SRBuildingSeg) for achieving super-resolution (SR) building extraction using relatively low-resolution remotely sensed images. SRBuildingSeg can fully utilize inherent information from the given low-resolution images to achieve high-resolution building extraction. In contrast to the existing building extraction methods, we first utilize an internal pairs generation module (IPG) to obtain SR training datasets from the given low-resolution images and an edge-aware super-resolution module (EASR) to improve the perceptional features, following the dual-encoder building segmentation module (DES). Both qualitative and quantitative experimental results demonstrate that our proposed approach is capable of achieving high-resolution (e.g. 0.5 m) building extraction results at 2×, 4× and 8× SR. Our approach outperforms 8 other methods with respect to the extraction result of mean Intersection over Union (mIoU) values by a ratio of 9.38%, 8.20% and 7.89% with SR ratio factors of 2, 4, and 8, respectively. The results indicate that the edges and borders reconstructed in super-resolved images serve a pivotal role in subsequent building extraction and reveal the potential of the proposed approach to achieve super-resolution building extraction. Our code is available at https://github.com/xian1234/SRBuildSeg.
Keywords
remote sensing imagery; building extraction; super-resolution; deep learning.
Subject
Environmental and Earth Sciences, Environmental Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
