The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B2-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2022, 315–320, 2022
https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-315-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2022, 315–320, 2022
https://doi.org/10.5194/isprs-archives-XLIII-B2-2022-315-2022
 
30 May 2022
30 May 2022

A BUILDING CHANGE DETECTION METHOD BASED ON A SINGLE ALS POINT CLOUD AND A HRS IMAGE

Z. Yang1,2, J. X. Chai1, Y. S. Zhang2, and S. H. Mei1 Z. Yang et al.
  • 1Hunan Provincial Key Laboratory of Hydropower Development Key Technology, PowerChina Zhongnan Engineering Corporation Limited, Changsha City, China
  • 2School of Geosciences and Info-Physics, Central South University, #932 South Road Lushan, Changsha, Hunan, China

Keywords: Building change detection, Shadow extraction, GrabCut, Point cloud data, Satellite image

Abstract. For common remote sensing image change detection based on different time phases, it is difficult to solve the problem of surface tilt caused by the different shooting angle and time, which makes it difficult to complete the accurate registration. Some scholars utilized three-dimensional data for change detection to avoid registration problem, however, it costs very high for using 3D data to detect changes. Aim at this problem, this paper proposes a method of combining a single phase of ALS (Airborne Laser Scanning) data and HRS (High Resolution Satellite) image. It is composed of the following four steps: (1) Extracting the shadow area of the new-phase optical image, and superimposing it with the classified old-time point cloud data, and then the disappeared building area and the approximate building area are obtained according to the associated relationship between the shadow area and buildings. (2) Determining the unchanged building areas as positive samples, and taking the remaining area and vegetation area after removing the approximate buildings from the image as negative samples, and then GrabCut algorithm is used to segment new-phase HRS images to obtain building areas. (3) Comparing between building areas obtained in the previous step and the registered old-time ALS data to obtain a new building area with noise. (4) Denoising the results of the previous step to obtain the final new building area. Two datasets are used to verify the method. The detection accuracy of the disappeared buildings is over 85%, and the detection accuracy of the newly added buildings is over 70%.