skip to main content
Language:
Search Limited to: Search Limited to: Resource type Show Results with: Show Results with: Search type Index

Water-Body Detection in Sentinel-1 SAR Images with DK-CO Network

Electronics (Basel), 2023-07, Vol.12 (14), p.3163 [Peer Reviewed Journal]

COPYRIGHT 2023 MDPI AG ;2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 2079-9292 ;EISSN: 2079-9292 ;DOI: 10.3390/electronics12143163

Full text available

Citations Cited by
  • Title:
    Water-Body Detection in Sentinel-1 SAR Images with DK-CO Network
  • Author: Xie, Youping ; Zeng, Haibo ; Yang, Kaijun ; Yuan, Qiming ; Yang, Chao
  • Subjects: Accuracy ; Algorithms ; Artificial satellites in remote sensing ; Classifiers ; Classifier–Optimizer (CO) ; Cluster analysis ; Clustering ; Datasets ; DBO-K-means (DK) ; Deep learning ; Dung ; Foraging behavior ; Indicators ; Labeling ; Labels ; Lakes ; Machine learning ; Management ; Mathematical optimization ; Microwave sensors ; Model testing ; Optimization ; Optimization algorithms ; Strategic management ; Surface water ; Swarm intelligence ; Synthetic aperture radar ; Synthetic Aperture Radar (SAR) ; Training ; Vector quantization ; Water ; Water resources ; water-body detection ; Workloads
  • Is Part Of: Electronics (Basel), 2023-07, Vol.12 (14), p.3163
  • Description: Synthetic Aperture Radar (SAR) is an active microwave sensor with all-day/night and all-weather detection capability, which is crucial for detecting surface water resources. Surface water-body such as rivers, lakes, reservoirs, and ponds usually appear as dark areas in SAR images. Accurate and automated extraction of these water bodies can provide valuable data for the management and strategic planning of surface water resources and effectively help prevent and control drought and flood disasters. However, most deep learning-based methods rely on manually labeled samples for model training and testing, which is inefficient and may introduce errors. To address this problem, this paper proposes a novel water-body detection method that combines optimization algorithms and deep learning techniques to automate water-body label extraction and improve the accuracy of water-body detection. First, this paper uses a swarm intelligence optimization algorithm, Dung Beetle Optimizer (DBO), to optimize the initial cluster center of the K-means clustering algorithm, which is called the DBO-K-means (DK) method. The DK method divides the training images into four categories and extracts the water bodies in them to generate the water-body labels required for deep learning model training and testing, and the whole process does not require human intervention. Then, the labels generated by DK and training data set images are fed into the Classifier–Optimizer (CO) for training. The classifier performs a dense classification task at the pixel level, resulting in an initial result image with blurred boundaries of the water body. Then, the optimizer takes this preliminary result image and the original SAR image as input, performs fine-grained optimization on the preliminary result, and finally generates a result image with a clear water-body boundary. Finally, we evaluated the accuracy of water-body detection using multiple performance indicators including ACC, precision, F1-Score, recall, and Kappa coefficient. The results show that the values of all indicators exceed 93%, which demonstrates the high accuracy and reliability of our proposed water-body detection method. Overall, this paper presents a novel DK-based approach that improves the automation and accuracy of deep learning methods for detecting water bodies in SAR images by enabling automatic sample extraction and optimization of deep learning models.
  • Publisher: Basel: MDPI AG
  • Language: English
  • Identifier: ISSN: 2079-9292
    EISSN: 2079-9292
    DOI: 10.3390/electronics12143163
  • Source: ROAD: Directory of Open Access Scholarly Resources
    ProQuest Central
    DOAJ Directory of Open Access Journals
Back to results list
INSPIRE LIBRARY - TON DUC THANG UNIVERSITY (84-028) 37 755 057 Feedback
19 Nguyen Huu Tho St. Dist.7, HCM thuvien@tdtu.edu.vn

Copyright © 2017 INSPIRE LIBRARY - TON DUC THANG UNIVERSITY

Searching Remote Databases, Please Wait