%0 Journal Article
%T An Algorithm of Multi-spectral Remote Sensing Image Segmentation Based on Edge Information<br>基于边缘的多光谱遥感图像分割方法
%A LIU Yong-xue
%A LI Man-chun
%A MAO Liang
%A <br>刘永学
%A 李满春
%A 毛亮
%J 遥感学报
%D 2006
%I 
%X According to the first geographic law of Tobler and the Marr's machine vision theory,an algorithm to segmenting multi-spectral remote sensing imageries has been put forward based on the edge information extracted from them.This algorithm consists of four steps listed below:(1) Detecting edge information in each band of remote sensing imageries using a improved Canny method;(2) Integrating edge information in each band of remote sensing imageries into a binary image by methods such as overlay technique in GIS technology,and then thinning edges in the binary image by techniques of mathematical morphology using a rectangle probe;(3) conjoining disconnected edges according to the characteristics of processing edge such as length,direction and so on,to close each region;(4) at last,labeling region and remove abundant edges that do not compose region.Then,the multi-spectral remote sensing imageries of Quickbird covering the Kumamoto city,Japan,have been taken as a case study for this algorithm,and the result has been compared with other segmentation algorithms such as Multi-Threshold Gray Slice Approach(MTGSA),Iterative Self-Organized Data Analysis Technology Algorithm(ISODATA) image segmentation algorithm,Watershed Segmentation Algorithm(WSA),Fractal Net Evolution Approach(FNEA) and so on.Based on the comparative analysis,conclusions could be drawn out that(1) In term of utilizing brightness information of each band,the scope that the algorithm proposed in the paper is the most comprehensive one,and MTGSA and WSA can only use single band of multi-spectral remote sensing image;(2) The result of this algorithm could be the most satisfied,as it detects edge information of each spectral band respectively,and then integrates as well as connects them together,maximally digging out the detailed features in remote sensing imageries;(3) In the aspect of computational duration,this algorithm is relatively a bit faster than others under the same environment.As the same as the other three approaches,the algorithm proposed in the paper has also confronted the common difficulty of how to confirm the coefficient in the image segmentation procedure.
%K edge detection
%K edge link
%K multi-spectral remote sensing image
%K image segmentation<br>边缘检测
%K 边缘生长
%K 多光谱遥感图像
%K 图像分割
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=E62459D214FD64A3C8082E4ED1ABABED5711027BBBDDD35B&cid=A41A70F4AB56AB1B&jid=F926358B31AC94511E4382C083F7683C&aid=10FF47A21D43C4AD&yid=37904DC365DD7266&vid=F3090AE9B60B7ED1&iid=38B194292C032A66&sid=406BF8ED3BCE1927&eid=35E8A259891FB32F&journal_id=1007-4619&journal_name=遥感学报&referenced_num=7&reference_num=14