融合层次聚类的高分辨率遥感影像超像素分割方法

Superpixel segmentation method of high resolution remote sensing images based on hierarchical clustering

为解决遥感影像分割尺度自动选取难的问题,提出了融合层次聚类的高分辨率遥感影像超像素分割方法。首先采用自适应形态重建的分水岭分割算法将影像分割成多个超像素;然后提取各超像素的灰度特征向量;最后利用层次聚类方法进行超像素合并,实现高分辨率遥感影像的精确分割。实验选用4组景遥感影像;采用定性和定量相结合的方法评价实验结果。实验结果表明,该方法有效提高了遥感影像分割精度,并取得了较好的分割视觉效果。

To solve the problem of automatic selection the segmentation scale in remote sensing image,a superpixel segmentation method of high resolution remote sensing image based on hierarchical clustering is proposed.Firstly,the watershed segmentation algorithm based on adaptive morphological reconstruction is used to segment the image into multiple superpixels.Then,the gray feature vectors of each superpixel is extracted.Finally,the hierarchical clustering method is adopted to merge the superpixels,the accurate segmentation of high-resolution remote sensing images is realized.Four sets of remote sensing images are selected in the experiment,and the experimental results are evaluated by a combination of qualitative and quantitative methods.Experimental results shown that the proposed method effectively improves the segmentation accuracy of remote sensing images,and better segmentation visual effects are obtained.