A Novel CA-RegNet Model for Macau Wetlands Auto Segmentation Based on GF-2 Remote Sensing Images
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Published:2023-11-09
Issue:22
Volume:13
Page:12178
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ISSN:2076-3417
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Container-title:Applied Sciences
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language:en
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Short-container-title:Applied Sciences
Author:
Li Cheng1ORCID, Cui Hanwen23, Tian Xiaolin13
Affiliation:
1. School of Computer Science and Engineering, Faculty of Innovation Engineering, Macau University of Science and Technology, Macau, China 2. School of Computer Science, Zhuhai College of Science and Technology, Zhuhai 519041, China 3. State Key Laboratory of Lunar and Planetary Sciences, Macau University of Science and Technology, Macau, China
Abstract
Wetlands, situated at the vital intersection of terrestrial and aquatic ecosystems, are pivotal in preserving global biodiversity and maintaining environmental equilibrium. The escalating trend of global urbanization necessitates the utilization of high-resolution satellite imagery for accurate wetland delineation, which is essential for establishing efficacious conservation strategies. This study focuses on the wetlands of Macau, characterized by distinctive coastal and urban features. A noteworthy enhancement in this study is the integration of the Coordinate Attention mechanism with the RegNet model, forming the CA-RegNet. This combined model demonstrates superior performance, outdoing previous Macau wetlands segmentation studies that used ResNet, evidenced by an approximate rise of 2.7% in overall accuracy (OA), 4.0% in the Kappa coefficient, 1.9% in the mAcc, and 0.5% in the mIoU. Visual evaluations of the segmentation results reinforce the competence of the CA-RegNet model in precisely demarcating coastal wetlands and Saiwan Lake, thereby overcoming the former constraints of ResNet and underscoring the robustness and innovation of this study.
Funder
Department of Education of Guangdong Province “Innovation and Strengthening Project” Scientific Research Project
Subject
Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science
Reference50 articles.
1. Nunziata, F., Ferrentino, E., Marino, A., Buono, A., and Migliaccio, M. (October, January 26). Monitoring Harsh Costal Environments Using Polarimetric SAR Data: The Case of Solway Firth Wetlands. Proceedings of the IEEE International Symposium on Geoscience and Remote Sensing IGARSS, Waikoloa, HI, USA. 2. Lin, X.F., Cheng, Y., Chen, G., Chen, W.J., Chen, R., Gao, D.M., Zhang, Y.L., and Wu, Y.B. (2023). Semantic Segmentation of China’s Coastal Wetlands Based on Sentinel-2 and Segformer. Remote Sens., 15. 3. WetNet: A Spatial-Temporal Ensemble Deep Learning Model for Wetland Classification Using Sentinel-1 and Sentinel-2;Hosseiny;IEEE Trans. Geosci. Remote Sens.,2021 4. Jiao, L.L., Sun, W.W., Yang, G., Ren, G.B., and Liu, Y.N. (2019). A Hierarchical Classification Framework of Satellite Multispectral/Hyperspectral Images for Mapping Coastal Wetlands. Remote Sens., 19. 5. Cross-scene wetland mapping on hyperspectral remote sensing images using adversarial domain adaptation network;Huang;ISPRS J. Photogramm. Remote Sens.,2023
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