Object-based change detection model using correlation analysis and classification for VHR image

In this paper we introduce an object-based change detection model using correlation analysis and classification. First we use eCognition to obtain an over-segmentation map. Then linear regression is used to gain three unique types of parameters - regression coefficient, offset, and correlation coefficient which can provide valuable information about the location and numeric change value derived within the segmentation objects in the two data sets. Understandably, the two data tend to be highly correlated when little change occurs, and uncorrelated when change occurs. Then we treat the three variables as a three-band image. Finally, we perform maximum likelihood classification with training examples. The result shows that our method performs better than the methods proposed by Yang [2] and J. Im [3]. The advantages of our method are that it performs automatically without selecting threshold empirically and alleviates the “salt and pepper” effect.