Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-4/W5, 227-232, 2015
http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XL-4-W5/227/2015/
doi:10.5194/isprsarchives-XL-4-W5-227-2015
© Author(s) 2015. This work is distributed
under the Creative Commons Attribution 3.0 License.
 
13 May 2015
MERGING AIRBORNE LIDAR DATA AND SATELLITE SAR DATA FOR BUILDING CLASSIFICATION
T. Yamamoto and M. Nakagawa Dept. of Civil Engineering, Shibaura Institute of Technology, 3-7-5 Toyosu, Koto-ku, Tokyo, Japan
Keywords: Urban Sensing, Building Extraction, Building Classification, Airborne LiDAR, Satellite SAR, Data Fusion Abstract. A frequent map revision is required in GIS applications, such as disaster prevention and urban planning. In general, airborne photogrammetry and LIDAR measurements are applied to geometrical data acquisition for automated map generation and revision. However, attribute data acquisition and classification depend on manual editing works including ground surveys. In general, airborne photogrammetry and LiDAR measurements are applied to geometrical data acquisition for automated map generation and revision. However, these approaches classify geometrical attributes. Moreover, ground survey and manual editing works are finally required in attribute data classification. On the other hand, although geometrical data extraction is difficult, SAR data have a possibility to automate the attribute data acquisition and classification. The SAR data represent microwave reflections on various surfaces of ground and buildings. There are many researches related to monitoring activities of disaster, vegetation, and urban. Moreover, we have an opportunity to acquire higher resolution data in urban areas with new sensors, such as ALOS2 PALSAR2. Therefore, in this study, we focus on an integration of airborne LIDAR data and satellite SAR data for building extraction and classification.
Conference paper (PDF, 2461 KB)


Citation: Yamamoto, T. and Nakagawa, M.: MERGING AIRBORNE LIDAR DATA AND SATELLITE SAR DATA FOR BUILDING CLASSIFICATION, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-4/W5, 227-232, doi:10.5194/isprsarchives-XL-4-W5-227-2015, 2015.

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