A Scale-Driven Change Detection Method Incorporating Uncertainty Analysis for Remote Sensing Images

Abstract

Change detection (CD) based on remote sensing images plays an important role in Earth observation. However, the CD accuracy is usually affected by sunlight and atmospheric conditions and sensor calibration. In this study, a scale-driven CD method incorporating uncertainty analysis is proposed to increase CD accuracy. First, two temporal images are stacked and segmented into multiscale segmentation maps. Then, a pixel-based change map with memberships belonging to changed and unchanged parts is obtained by fuzzy c-means clustering. Finally, based on the Dempster-Shafer evidence theory, the proposed scale-driven CD method incorporating uncertainty analysis is performed on the multiscale segmentation maps and the pixel-based change map. Two experiments were carried out on Landsat-7 Enhanced Thematic Mapper Plus (ETM+) and SPOT 5 data sets. The ratio of total errors can be reduced to 4.0% and 7.5% for the ETM+ and SPOT 5 data sets in this study, respectively. Moreover, the proposed approach outperforms some state-of-the-art CD methods and provides an effective solution for CD.