Remote Sensing Image Water Body Extraction Method Combining Local and Global Features in the CV Model
Accurate and effective extraction of water body information is crucial for water resource management,monitoring,and application. The diversity in the shape,size,and distribution of water bodies,coupled with the complexity of scenes,poses challenges in efficiently and accurately extracting water bodies from remote sensing images. Existing active contour model algorithms for water extraction are primarily tailored for specific data types or water body types and are significantly affected by noise,often resulting in unclear segmentation boundaries and low accuracy in water extraction. In response to these issues,this paper proposes a rapid segmentation method using the Chan-Vese (CV) model that integrates both local and global features of the target. The energy functional of this improved method comprises global,local,and regularization terms. By incorporating local image information into the CV model's energy functional and introducing convolution operators in the local term to compute the mean grayscale difference between the interior and exterior of the evolution curve,using difference images instead of the original images effectively limits erroneous movements during the processing of uneven grayscale images. Additionally,the regularization term consists of a length constraint and a new penalty energy. The length constraint effectively limits the evolution curve's length,preventing excessive boundary gradients and resulting in smoother and more precise target boundaries. The penalty energy avoids the re-initialization steps common in traditional level set methods,enhancing efficiency. This paper utilizes complex land background images from Sentinel-1 and Sentinel-2 remote sensing satellites to validate the practicality of the proposed algorithm. Experiments on the segmentation of lakes,rivers,and small water bodies in remote sensing images show that for SAR (Synthetic Aperture Radar) images,the improved CV model achieves segmentation accuracies of 96.15％,95.19％,and 83.64％ with F1 scores of 95.77％,91.06％,and 75.78％,respectively. For optical images,the accuracies are 97.71％,95.12％,and 93.97％,with F1 scores of 97.15％,93.67％,and 86.78％,respectively. In urban central areas,the SAR data segmentation accuracy and F1 score are 97.2％ and 89.2％,respectively;the optical data accuracy and F1 scores are 92.12％ and 89.37％. The improved algorithm demonstrates high segmentation accuracy for complex,multi-type water bodies and urban water bodies,achieving high-precision water body extraction in remote sensing images,thus proving highly practical.
optical imageSAR imagewater body extractionCV modelenergy functionallocal termdifference image
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