基于对象的对应分析在高分辨率遥感影像变化检测中的应用

Object-Based Correspondence Analysis for Improved Accuracy in Remote Sensing Change Detection

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.