CHANGE DETECTION FOR HIGH-RESOLUTION REMOTE SENSING IMAGERY USING OBJECT-ORIENTED CHANGE VECTOR ANALYSIS METHOD

Change Vector Analysis (CVA) is an important change detection method for remote sensing imagery with medium and low resolution. Traditional CVA is a pixel-based method, which is insufficient for high-resolution (HR) imagery. An object-oriented change vector analysis method (OCVA) is proposed in the paper. Image segmentation method was used to get image objects. The object histogtam was extracted as the feature of the image object at different bands. G statistic was employed to measure the distance between the object histograms at two times. Change vector of the object was built by the histogram distance at different bands. The heterogeneity of the object was measured by the magnitude of the object change vector. The threshold to detect change/no change objects was decided by Otsu method which maximizes the variance between change objects and unchanged objects. The experimental results on QuickBird images show that OCVA can make full use of the spatial information in HR images and get higher accuracy than pixel-based CVA.