传统模糊C均值聚类算法在遥感图像水体分割中抑制噪声的能力弱且需人为设定的参数太多。针对这一问题,提出了一种结合引力模型的模糊聚类遥感图像水体分割方法。首先将模糊C均值聚类算法得到的模糊隶属度矩阵作为本文算法的初始隶属度矩阵,然后计算像素灰度值的局部标准差与局部均值的比率,并将归一化的比率作为加权因子来反映邻域像素对中心像素的影响程度,最后,结合空间吸引模型,在局部空间和模糊隶属度关系中引入权衡加权因子,同时考虑中心像素与其相邻像素之间的空间距离和类别隶属度差异,自适应准确地估计来自相邻像素的空间约束,使权衡加权因子完全适应图像内容。实验结果表明,相较于传统的模糊C均值聚类算法以及近年来相关的代表性算法,本文算法的分割性能具有明显的优势,分割准确度均为最高且最高达到97.1%,虚警率下降约15%~30%。

The conventional fuzzy C-means clustering algorithm is weak in terms of noise suppression in remote sensing image water segmentation and requires the manual setting of excessive parameters. To solve this problem, a fuzzy clustering remote sensing image water segmentation method combined with a gravity model was proposed. First, the fuzzy membership matrix obtained using the fuzzy C-means clustering algorithm was used as the initial membership matrix of the algorithm. Then, the ratio of the local standard deviation of the pixel gray value to the local mean was calculated. Furthermore, the normalized ratio was used as a weighting factor to reflect the influence of neighboring pixels on the central pixel. Finally, combined with the spatial attraction model, a tradeoff weighting factor was introduced in the relationship between the local space and fuzzy membership, which could simultaneously consider the spatial distance between the central pixel and its neighboring pixels and the difference in category membership. This factor can adaptively and accurately estimate spatial constraints using the neighboring pixels and fully adapt to the image content. Experimental results show that compared with the conventional fuzzy C-means clustering algorithm and related representative algorithms developed recently, the segmentation performance of the proposed algorithm is optimum. The proposed algorithm exhibits the highest segmentation accuracy, with a maximum of 97.1%, and the false alarm rate is reduced by approximately 15%--30%.