Change detection using the object features

For the detection of changes, several statistical techniques exist. When adopted to high-resolution imagery, the results of the traditional pixel-based algorithms are often limited. Especially if small structural changes are to be detected, object- based procedures show promises. In the given paper, we propose an unsupervised object-based change detection and change classification approach based on the object features. Following the automatic pre-processing, image objects and their object features are extracted. Change detection is performed by the multivariate alteration detection (MAD), accompanied by the maximum autocorrelation factor (MAF) transformation. The change objects are then classified using the fuzzy maximum likelihood estimation (FMLE). Finally the classification of changes is improved by probabilistic label relaxation.