TY - GEN
T1 - MERGING AIRBORNE LIDAR DATA AND SATELLITE SAR DATA FOR BUILDING CLASSIFICATION
AU - Yamamoto, T.
AU - Nakagawa, M.
N1 - Publisher Copyright:
© 2015, Copernicus. All rights reserved.
PY - 2015/5/11
Y1 - 2015/5/11
N2 - 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.
AB - 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.
KW - Airborne LiDAR
KW - Building Classification
KW - Building Extraction
KW - Data Fusion
KW - Satellite SAR
KW - Urban Sensing
UR - http://www.scopus.com/inward/record.url?scp=84933040725&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84933040725&partnerID=8YFLogxK
U2 - 10.5194/isprsarchives-XL-4-W5-227-2015
DO - 10.5194/isprsarchives-XL-4-W5-227-2015
M3 - Conference contribution
AN - SCOPUS:84933040725
T3 - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives
SP - 227
EP - 232
BT - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives
A2 - Fuse, Takashi
A2 - Nakagawa, M.
PB - International Society for Photogrammetry and Remote Sensing
T2 - ISPRS WG IV/7 and WG V/4 Joint Workshop on Indoor-Outdoor Seamless Modelling, Mapping and Navigation
Y2 - 21 May 2015 through 22 May 2015
ER -