Object-Based Correspondence Analysis for Improved Accuracy in Remotely Sensed Change Detection
Hao Gong +, Jinping Zhang and Shaohong Shen
School of Remote Sensing & Information Engineering, Wuhan University
129 Luoyu Road, Wuhan 430079, China
Abstract. The correspondence analysis (CA) method, a multivariate technique widely used in ecology, is relatively new in remote sensing. In the CA differencing method, bi-temporal images were transformed into component space, and individual component image differencing can then be performed to detect possible 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 spectral 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. This paper proposes an object-based approach to correspondence analysis for change detection, whose performance was compared with those of pixel-based PCA and CA. Results showed that the object-based CA method produced the best accuracy in change detection.
Keywords: change detection, correspondence analysis, objects, accuracy
In: Wan, Y. et al. (eds) Proceeding of the 8th international symposium on spatial accuracy assessment in natural resources and environmental sciences, World Academic Union (Press).