结合目标局部和全局特征的CV模型遥感影像水体提取方法

Remote Sensing Image Water Body Extraction Method Combining Local and Global Features in the CV Model

如何准确有效的实现水体信息提取,是目前水资源管理、监测和应用非常重要的一环,由于水体形状、大小和分布的多样性以及场景的复杂性,如何高效准确地从遥感影像中提取出水体仍具有挑战性。现有的主动轮廓模型水体提取算法主要针对某一数据或特定水体类型,且受到噪声影响较大等问题,导致水体提取精度不高。因此,提出一种结合目标局部和全局特征的CV(Chan-Vese)模型快速分割方法。该改进方法的能量泛函由整体项、局部项和正则项组成,通过将局部图像信息融入CV模型的能量泛函中,在局部项中引入卷积算子并计算演化曲线内外部差值图像灰度均值,用差分图像代替原始图像,有效限制演化曲线处理灰度不均匀图像时发生的错误移动。此外,正则化项由长度约束项和新的惩罚能量组成,约束了演化曲线的长度,使目标边界更加平滑、精确,同时避免了传统水平集方法中的重新初始化步骤,以提高效率。针对哨兵1号卫星和哨兵2号卫星影像中的湖泊、河流和小水体分割实验结果表明:对于SAR(Synthetic Aperture Radar)影像,改进后的CV模型的分割精度分别达到96.15%、95.19%、83.64%,F1分数达到95.77%、91.06%、75.78%;对于光学影像,分割精度分别达到97.71%、95.12%、93.97%,F1分数达到97.15%、93.67%、86.78%。针对城市中心区域水体分割,SAR数据分割精度和F1分数分别为97.2%和89.2%;光学数据分割精度和F1分数分别为92.12%和89.37%。改进算法对背景复杂的多类型水体和城市区域水体均有较高的分割精度,能够实现遥感图像中水体的高精度提取。

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.