摘要
提出了一种基于对象的对应分析(correspondence analysis,CA)差值法对高分辨率遥感影像进行变化检测,并将结果与基于像素的主成分分析(principal component analysis,PCA)差值法和CA差值法进行了比较。实验证明,基于对象的CA差值法提高了变化检测的精度。
The correspondence analysis （CA） method, a multivariate technique widely used in ecology, is relatively new in remote sensing. In the CA differencing method, hi-temporal images are transformed into component space, and individual component image differencing can be performed to detect potential changes , somehow similar to principal component analysis.The advantage of the CA method is that more variance of the original data can be captured in the first component than in the PCA method. However, these techniques are all performed on a pixel by pixel basis, becoming unsatisfactory in some circumstances due to higher spec- tral heterogeneity in imagery of high spatial resolution. This problem can be alleviated by the object-based strategy, which segments the image into regions of relative homogeneity, which are, in turn, used as the basic units for data analysis. We proposed an object-based CA approach for change detection, whose performance was compared with those of pixel-based PCA and CA. Experimental results show that the object-based CA method produces the best accuracy in change detection.
出处
《武汉大学学报（信息科学版）》
EI
CSCD
北大核心
2009年第5期544-547,551,共5页
Geomatics and Information Science of Wuhan University
基金
国家973计划资助项目(2006CB701302)
湖北省科学技术研究与开发资金资助项目(2007ABA276)
关键词
变化检测
对应分析
对象
精度
change detection
correspondence analysis
objects
accuracy
