Change detection based on stacked generalization system with segmentation constraint

Change detection based on a multi-classifier ensemble system can take advantage of multiple classifiers to extract change information in remote sensing images. In this paper, an efficient heterogeneous ensemble algorithm, i.e., the stacked generalization (SG) combined with image segmentation, is proposed to construct a simple multi-classifier ensemble system that can offer better detection accuracy with lower computational cost. Due to the rich spatial information in high-spatial-resolution remote sensing images, structure texture (morphological) and statistical texture features are extracted to construct the input data to the ensemble system along with spectral features. In addition, constrained analysis on segmented objects integrates the smaller heterogeneity segmentation map and pixel-wise change map to generate the final change map. The experiments were carried out on two ZY-3 and a QuickBird dataset. The results show that the proposed algorithm can integrate the advantages of both pixel-wise ensemble and object-oriented methods, and effectively improve the accuracy and stability of change detection.