Sub-Graph Matching-Based Building Changes Detection Using High-Resolution Remote Sensing Images

High-resolution remote sensing (HRRS) images of urban regions have large viewing angle variations, significant noise jamming, and obvious building shadows. Hence, deviation and distortion usually occur to the buildings in HRRS images collected at different phases (multi-temporal HRRS images). The traditional detection methodology is ineffective for accurate and efficient building changes detection in multi-temporal HRRS images. In order to address these problems, this paper proposes a sub-graph matching-based building changes detection (SGMBCD) scheme. First, this paper presents a Graphcut-based buildings extracting (GCBE) method from multi-temporal HRRS images. Next, a sub-graph matching-based registration (SGMR) method is devised to register the previously extracted buildings from multi-temporal HRRS images and to obtain matched ASIFT feature point pairs and singular points. Finally, singular points and overlay analysis-based method (SPOA) is developed to detect building changes in multi-temporal HRRS images. The types of building changes included in this paper are changes (e.g., erection, dismantling, repairing, and reconstruction) and non-changes. In order to demonstrate the effectiveness of the proposed SGMBCD scheme, it is compared with five typical algorithms (i.e., BCDBPM, SDBBCD, BCDBICO, NCUTBCD, and RSMBCD) on three sets of multi-temporal WorldView2 test images. Experimental results show that in comparison with the other methods, SGMBCD can effectively address the challenging problem of building changes detection in multi-temporal HRRS images. The average recall ratio, precision ratio and F value is 91.47%, 86.49% and 88.91% respectively, and the average time consumption is 60.3 s. This demonstrates SGMBCD can detect building changes in multi-temporal HRRS images accurately and efficiently.