一种融合超像素与最小生成树的高分辨率遥感影像分割方法

摘要

Image segmentation is the basic and key step of object-oriented remote sensing image analysis.Conventional image segmentation method is sensitive to image noise and hard to determine the correct segmentation scale.To solve these problems, a novel image segmentation method by combining superpixels with minimum spanning tree was proposed in this paper.First, the image is over-segmented by simple linear iterative clustering algorithm to obtain superpixels.Then, superpixels are firstly clustered by regionalization with dynamically constrained agglomerative clustering and partitioning algorithm using the initial segmentation number and the sum of squared deviations (SSD), local variance (LV), rate of LV change (ROC-LV) index of graphs corresponding to the segmentation number are obtained.So the suitable image segmentation number is determined according to the SSD, LV, ROC-LV index of graphs corresponding to segmentation number.Finally, superpixels are reclustered by regionalization with dynamically constrained agglomerative clustering and partitioning algorithm based on the suitable segmentation number.The experimental results showed that the proposed method can obtain good segmentation results.%影像分割是面向对象高分辨率遥感影像分析的基础与关键.针对传统影像分割方法易受噪声影响,且难以确定合适的影像分割尺度的问题,本文提出了一种融合超像素与最小生成树的高分辨率遥感影像分割方法.首先用简单线性迭代聚类算法对影像进行过分割生成超像素;然后初始设定影像分割数,采用区域动态约束聚类算法对超像素进行合并,获得分割数-方差和、分割数-局部方差、分割数-局部方差变化率指标图,依据3个指标图确定合适的影像分割数;最后根据确定的合适影像分割数,采用区域动态约束聚类算法对超像素重新合并得到分割结果.定性对比试验和定量评价结果表明,本文方法可以有效地克服影像噪声对分割结果的影响,获得良好的影像分割结果.