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Automated building change detection using UltraCamD images and existing CAD data

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Abstract:

The aim of this research is to develop a technique that can detect changes in urban buildings after natural disasters such as earthquakes, typhoons or tsunamis. The increasing demands for real-time and effective change detection methodologies for urban buildings, including collapsed, height-changed and removed buildings, require the use of high spatial resolution images. Conventional change detection techniques, developed for medium resolution remotely sensed data, are neither effective nor efficient due to their difficulties in discriminating real changes from changes caused by change in perspective view, as well as due to the increased spatial detail in texture and edges in high spatial resolution images. In this paper, an automatic change detection method, using UltraCamD images and existing 3-dimensional computer-aided design (CAD) data, which analyse changes at the building level, is presented. The edge features of every building in the image are first extracted, using the following steps: pre-processing, edge extraction using a Canny detector, edge denoising, and line segmentation. Then, the silhouettes of each building, which are extracted from the CAD model based on camera parameters, are projected onto the image and are compared to the edge features of each building in the image. The portion Hausdorff matching methodology is employed to calculate the similarities between the projected silhouettes of each building from the CAD model, and the edge features of each building are extracted from the image. Using the calculated similarity, collapsed or removed buildings can be detected. The maximal similarity between the edge features and the projected silhouettes in the direction of projection is used to detect changes in building height. The methods are illustrated with an airborne UltraCamD image over the city of Tokyo, Japan. The results indicate that among 1320 buildings (10 were collapsed) used in the test, 45 buildings were successfully detected to have had collapsed or been removed. Nine out of the forty-five buildings were correctly detected from the list of 10 collapsed buildings. Overall, 2885 buildings were used to test the method for detecting changes in building height, of which 11 of the 27 buildings known to have changed heights with the CAD model were successfully detected. However, our methodology faces challenges from factors such as CAD model errors, high building density, and small changes in building height.

Document Type: Research Article

DOI: http://dx.doi.org/10.1080/01431160903475340

Affiliations: 1: Center of Information & Network Technology, Beijing Normal University, Beijing, China 2: State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Institute of Remote Sensing Applications, Chinese Academy of Sciences and Beijing Normal University, Beijing, China 3: College of Resource Science & Technology, Beijing Normal University, Beijing, China 4: Naval Armament Academy, Beijing, China 5: Institute of GIS, PASCO Corp, Tokyo, Japan

Publication date: February 1, 2010

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