基于Mean-shift影像分割算法的TM影像分类及城市扩展研究

Study of TM Image Classification Based on Image Segmentation of Mean-Shift Algorithm and Its Application in Urban Sprawl

以马来西亚雪兰莪州及首都吉隆坡为例,将Mean-shift算法的影像分割方法应用于研究区的TM影像分类中,较传统方法的分类精度更高,可满足应用需求。依据该分类结果对研究区的空间城市化特征进行分析,发现近期研究区一直处于高速扩展阶段,外延式扩展和填充式扩展并行发展成为该研究区城市扩展的主导模式。

Image segmentation is one of the methods of image classification. In this paper,Selangor Malaysia and Kuala Lumpur, the capital of Malaysia, were taken as an example and the Mean-shift algorithm, which is one of the methods of image segmentation, was employed to do Landsat/TM image classification. Then the classification result was used to do urban sprawl characteristic analysis. The classification result got from the image segmentation method based on Mean-shift algorithm shows that the precision is very high. The process of urban sprawl belongs to the combination of extension sprawl model and filling sprawl model. The result shows that TM image classification, based on the image segmentation of Mean-shift algorithm provides a new approach with high accuracy for TM image classification.