The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Publications Copernicus
Articles | Volume XXXIX-B4
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XXXIX-B4, 73–78, 2012
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XXXIX-B4, 73–78, 2012

  27 Jul 2012

27 Jul 2012


N. Ishimaru1, K. Iwamura1, Y. Kagawa2, and T. Hino2 N. Ishimaru et al.
  • 1Hitachi, Ltd., 1-18-13, Soto-Kanda, Chiyoda-ku, Tokyo, Japan
  • 2Hitachi Solutions, Ltd., 4-12-7, Higashishinagawa, Shinagawa-ku, Tokyo, Japan

Keywords: Change Detection, Updating, Building, Urban, High resolution, Quickbird

Abstract. This paper presents a novel structural image analysis method based on geometric optimization techniques towards automatic building change detection. The aim of this method is to efficiently detect the changes of various buildings such as small houses and houses with complex roof in an urban area from high resolution satellite imagery by comparing with spatial database (maps). The previous research has indicated of the effectiveness of a map-based building change detection approach, and further investigation suggests the following three problems; (1) the large diversity of building types, roof shape, roof materials, illumination condition and shadow, (2) the difficulty of imagery and maps matching which normally leads to considerable position error, (3) the capacity of extracting various types of newly-built buildings. To solve these problems, we propose a new geometric optimization method which consists of the following two steps; (1) the building recognition based on a combinatorial optimization method for optimal building boundary extraction, (2) the newly-built building extraction based on an optimal building hypothesis search method. The experimental results showed that the detection rate was approximately 89% for existing and changed buildings, and approximately 83% for newly-built buildings. These results demonstrate the effectiveness of the proposed geometric optimization methods to integrate bottom-up and top-down analysis. By combining the locally detected image features with consideration of regional contexts from map, our method can achieve highly accurate building change detection in urban area. The method has been applied to a building change detection service named "HouseDiff" and succeeded in assisting users.