一种边界引导的多尺度高分辨率遥感图像分割方法

EDGE-GUIDED SEGMENTATION METHOD FOR MULTISCALE AND HIGH RESOLUTION REMOTE SENSING IMAGE

针对高分辨率遥感图像分割过程中区域合并复杂性问题,提出了一种基于边界引导的多尺度遥感图像分割算法.一方面应用SUSAN算子提取高分辨率遥感图像中地物的边界用于限制区域增长过程;另一方面进行两阶段增长,首先应用基于图的分割算法进行基于像素的区域生长,然后进行考虑对象特征信息的区域合并.对宜昌城区某处融合后的QuickBird彩色图像进行了实验,并分别与无边界引导分割以及eCognition平台下图像分割效果进行了对比.结果表明,该方法可以有效抑制传统图像分割算法在低对比度区的区域融合问题,突破了分割尺度参数不能在全图取得合理分割的局限性.

In order to overcome the complexity of region merging in the segmentation of high resolution remote sensing images, an edge-guided segmentation method for muhi-scale and high resolution remote sensing image was proposed. First, SUSAN operator was used to extract feature edges from the original test image. Then, a graph-based segmentation algorithm was used in the first-stage image segmentation and the following region merging stage, and the extracted edges were efficiently used to guide merging process. To validate the proposed method, two experiments were performed on QuickBird image. The results were compared with the segmentation results of eCoguition and method without edge-guide. The results show that this proposed method can efficiently depress the region merging in low-contrast areas for the traditional image segmentation algorithms, and make it possible to choose a reasonable segmentation scale in the whole image.