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Detection and Refinement of Water Bodies Using a Local Thresholding Approach with Feature Extraction from Radar Satellite Images (Case study: Zaribar and Chitgar Lakes)
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Farzaneh Naeimi Asl   , Mehdi Akhoondzadeh * |
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Abstract: (208 Views) |
The water scarcity caused by recent droughts, climate change, population growth, and excessive consumption of water resources has had widespread impacts on the lives of humans, animals, and plants. Iran, due to its geographical location, climate changes, and lack of water resources, is on the verge of a water crisis. Surface water bodies, such as lakes, are also affected by this crisis. Therefore, proper monitoring, control, and management of water resources are essential. This monitoring can be carried out quickly and accurately through the use of satellite images, providing continuous reports on the status of surface water resources. In this research, the water body surface area of Zaribar Lake in Kurdistan Province was determined using radar satellite images through a local thresholding approach. This approach consists of three main steps in its implementation. In the first step, under a feature extraction process, four distinct categories of features—namely: texture, mathematical, geometric, and polarimetric features—were extracted from the primary radar image. Then, a classification process was conducted using four machine learning classification models, resulting in an initial classified image of the area. In the second step, a global threshold was applied to the radar image of the region, resulting in the identification of the primary water cluster in the area. In the final step, to refine and improve the initial water cluster, a local thresholding process was performed. In this process, based on the characteristics of the area, the type, number, and location of existing land uses were considered, and local thresholds for each cluster in the region were determined separately by calculating probability density functions (PDFs). By applying the local thresholds and then imposing a series of hydrological constraints, the final map of surface water was generated. The results obtained from the proposed approach in this research indicate that local thresholding succeeded in detecting and improving the surface water extent with accuracies of 95.44% and 98.27% corresponding to AUC and F1 score criteria, respectively. Additionally, a change detection experiment of Chitgar Lake was implemented to challenge the effectiveness of the proposed approach. The results of this experiment, with accuracies of 94.55% and 96.65% corresponding to AUC and F1 score criteria, respectively, validated the efficacy of the proposed approach.
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Article number: 3 |
| Keywords: remote sensing radar, feature extraction, machine learning, local thresholding, probability density function (PDF) |
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Full-Text [PDF 2505 kb]
(122 Downloads)
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Type of Study: Research |
Subject:
Photo&RS
Received: 2024/06/26
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Naeimi Asl F, Akhoondzadeh M. Detection and Refinement of Water Bodies Using a Local Thresholding Approach with Feature Extraction from Radar Satellite Images (Case study: Zaribar and Chitgar Lakes). JGST 2024; 14 (2) : 3 URL: http://jgst.issgeac.ir/article-1-1191-en.html
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