Change detection in high-resolution images based on feature importance and ensemble method

A robust framework for change detection in high-resolution (HR) images which takes into account feature importance of morphological attribute profiles (APs) is proposed. Although APs allow the discriminative extraction of geometrical features, the selection of optimal filtering thresholds is always a challenging task. In order to address this problem, the importances of multi-attribute APs, which are generated by ranges of thresholds, are calculated in advance. The APs with higher importances are chosen as input features for change detectors. Furthermore, owing to the possible incomplete feature importance model caused by randomness and small size of selected training samples through random forest, an ensemble method is conducted by integrating different results to enhance the stability of the final output. Experimental results obtained by two pairs of bi-temporal HR images demonstrate the effectiveness of the proposed approach.