Post-Classification Change Detection of High Resolution Satellite Images Using AdaBoost Classifier

This paper presents a post-classification approach that can achieve efficient results for change detection by using AdaBoost classifier. In the first step, a land cover of satellite image is classified in the independent fashion. For the stable classification of man-made structures, 3D features are employed. 3D co-occurrence feature is used with 2D co-occurrence feature and Harr-like feature. Adaboost classifier is implemented for the classification of a land cover into 4 classes: road, grass/bare-ground, foliage and building. In the second step, we perform the change detection for multi-temporal images. We carry out the simple comparison between terrain classification results. The experimental results show that the resulting change map efficiently reflects a newly developed area.