An automatic change detection method for monitoring newly constructed building areas using time-series multi-view high-resolution optical satellite images

Abstract

Automatically monitoring newly constructed building areas (NCBAs) is essential for efficient land resource management and sustainable urban development, particularly in the rapidly urbanizing country of China. In this regard, time-series multi-view high-resolution optical satellite images can provide fine spatial details for clearly characterizing NCBAs, but this leads to great heterogeneity and complexity, owing to the high spectral variation, complicated imaging conditions, and different viewing angles. Moreover, to date, the vertical features and time-series information from these images have not been fully exploited for urban change detection. In this paper, our primary objective is to automatically detect the presence of NCBAs, and meanwhile, to investigate the feasibility of identifying their change timing using time-series multi-view ZY-3 high-resolution satellite images. To this aim, we propose an automatic change detection method consisting of three components: 1) firstly, we jointly use planar-vertical features to delineate the NCBAs; 2) object-based temporal correction is subsequently applied to improve the spatiotemporal consistency of the features; and 3) finally, a multi-temporal change detection model is used to simultaneously capture the NCBAs and the change timing. We applied the method on two urban fringe areas of Beijing (7 multi-temporal image sets) and Shanghai (7 multi-temporal image sets), respectively, which are cities that have been experiencing rapid urbanization. The experimental results confirmed the effectiveness of the proposed method. For both study areas, the F-score values reached nearly 90% in terms of NCBA detection, and with respect to the change timing, the overall accuracies with a one-year tolerance strategy reached around 92%. The joint use of the planar-vertical features and the inclusion of multi-temporal images make the proposed method a promising approach for automatically providing the spatiotemporal information of NCBAs in practical applications.