摘要
高分辨率遥感图像中的细小目标(如道路等)使图像的同类区域表现变得不一致,从而增加了高分辨率图像聚类的难度,本文提出了一种高分辨率遥感图像的聚类方法,其聚类过程包括如下三个步骤:第一步,在滑动窗口内使用消除次要成分法处理遥感图像,该处理过程使用一维形态学分水岭技术获得直方图中的左侧阈值和右侧阈值,再根据这两个阈值滤除图像中的次要成分;第二步,计算滑动窗口内的图像特征;第三步根据图像特征量利用BPC(Back Propagation and Competitive)网络进行图像聚类。三组试验(本文提出的聚类算法,最邻近距离聚类法,K均值聚类法)表明本文提出的图像聚类方法可以有效实现高分辨率遥感图像的聚类。
The technology of clustering high resolution imagery is difficult, due to the fact that the minor components, such as roads, make the appearance of the same category region non-uniform. This paper proposes a new approach to cluster high resolution remote sensing imagery. The clustering approach includes three steps. First, eliminate the minor components in moving windows. The process uses 1-D morphological watershed technique to find the left threshold and the right threshold in the histogram. The gray levels beyond the two thresholds which result from minor components will replaced by the principle gray level. This process can improve the statistic measures when the moving windows contain some small hetero-objects. Second, compute the image characteristics in moving windows. Third, apply BPC neural network, which is combined by a back-propagation network and a competitive network, to cluster images according to the images characteristics. Three approaches are tested using SPOT images for clustering residential areas and agricultural areas in the suburb of Beijing. The experimental results show that the new clustering approach has the highest clustering accuracy.
出处
《电子与信息学报》
EI
CSCD
北大核心
2003年第8期1073-1080,共8页
Journal of Electronics & Information Technology
关键词
遥感图像
累量
BPC网络
图像聚类
Cumulant, Eliminating the minor components, BPC neural network, High reso- lution remote sensing imagery, Clustering
