高分辨率遥感影像超像素的模糊聚类分割法

Superpixel segmentation method of high-resolution remote sensing image based on fuzzy clustering

传统模糊C均值聚类在影像分割中只考虑影像的灰度特征,导致该算法用于高空间分辨率遥感影像分割时分割结果不理想。针对该问题,本文提出了一种高分辨率遥感影像超像素的模糊聚类分割方法。该方法首先利用分水岭变换算法产生多个超像素子区域;然后比较各个子区域间光谱特征的相似性;最后利用融合光谱特征的模糊C均值聚类对这些超像素子区域进行合并。试验选用4组不同场景的遥感影像,采用定性和定量相结合的方法评价试验结果。试验结果表明,该方法有效提高了分割区域的分割精度,并取得了较好的分割视觉效果。

Traditional fuzzy C-means clustering(FCM)only considers the gray features of image in image segmentation,which results in unsatisfactory segmentation results when the algorithm is applied to high-resolution remote sensing image segmentation.In order to solve this problem,a new method of superpixel segmentation method of high-resolution remote sensing image based on fuzzy C-means clustering is proposed in this paper.Firstly,watershed transform algorithm is used to generate multiple superpixels,and then the similarity of spectrum features among superpixels are compared.Finally,these superpixels are merged by a FCM method combined with spectrum features.Four sets of remote sensing images of different scenes were selected in the experiment,and the experimental results were evaluated by combining qualitative and quantitative methods.The experimental results show that the method can effectively improve accuracy of the segmentation and achieve better visual effect of the segmentation.