Object-oriented change detection based on the Kolmogorov–Smirnov test using high-resolution multispectral imagery

In this article, we propose a novel method of object-oriented change detection for high-resolution remote-sensing imagery. The method consists of three main parts: image segmentation, object adjusting and change detection. We use the Fractal Net Evolution Approach to segment the multi-temporal images. Then we adjust the object maps. By merging the objects in relatively large areas, the object -adjusting algorithm aims to obtain a set of objects with different sizes, which coincide better with the real ground objects than the single-scale results. In the third part, the Kolmogorov–Smirnov two-sample test detects each pair of objects in the multi-temporal object maps with multi-scale. The calculated value of the D-statistic is compared to the threshold of a user-defined significance level. Through these three processes, we can make full use of the spatial and spectral features in high- resolution images to detect changes. According to our experiments in two study areas employing QuickBird imagery, the overall errors of our method decreased by more than 1000 pixels compared with the conventional object-oriented change vector analysis. The proposed method can also avoid the errors resulting from classification in the method of post-classification comparison.