Abstract
Lakes play an important role in the water ecosystem on earth, and are vulnerable to climate change and human activities. Thus, the detection of water quality changes is of great significance for ecosystem assessment, disaster warning and water conservancy projects. In this paper, the dynamic changes of the Poyang Lake are monitored by Synthetic Aperture Radar (SAR). In order to extract water from SAR images to monitor water change, a water extraction algorithm composed of texture feature extraction, feature fusion and target segmentation was proposed. Firstly, the fractal dimension and lacunarity were calculated to construct the texture feature set of a water object. Then, an iterated function system (IFS) was constructed to fuse texture features into composite feature vectors. Finally, lake water was segmented by the multifractal spectrum method. Experimental results showed that the proposed algorithm accurately extracted water targets from SAR images of different regions and different imaging modes. Compared with common algorithms such as fuzzy C-means (FCM), the accuracy of the proposed algorithm is significantly improved, with an accuracy of over 98%. Moreover, the proposed algorithm can accurately segment complex coastlines with mountain shadow interference. In addition, the dynamic analysis of the changes of the water area of the Poyang Lake Basin was carried out with the local hydrological data. It showed that the extracted results of the algorithm in this paper are a good match with the hydrological data. This study provides an accurate monitoring method for lake water under complex backgrounds.
Highlights
Since remote sensing images with long time series and largescales can be used to study the change of water areas dynamically, remote sensing technology is of great significance in studying the rules of water areas [8,9]
A water extraction algorithm from the Synthetic Aperture Radar (SAR) images of the lake water is proposed, which consists of texture feature extraction, feature fusion and target segmentation
Experimental results show that the proposed algorithm accurately extracts water targets from SAR images of different regions and different imaging modes
Summary
Inland lakes are an important part of the biochemical and hydrological cycles of the earth, which are very vulnerable to climate change and human activities. The dynamic changes of water such as water scope, water level/depth, flow rate and water quality of the lake are closely related to flood or drought disasters, biodiversity and ecological protection, and are closely related to human activities such as agricultural development and urbanization construction [1,2,3,4]. The traditional way to monitor lakes is to set up hydrological monitoring stations for observation. Monitoring stations are few or inadequate in many remote regions, it is extremely difficult to carry out dynamic analysis and research on large-scale lakes due to limited observation information. Since remote sensing images with long time series and largescales can be used to study the change of water areas dynamically, remote sensing technology is of great significance in studying the rules of water areas [8,9]
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