Building classification using airborne lidar data with satellite SAR data

Tatsuya Yamamoto, Masafumi Nakagawa

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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. On the other hand, SAR data have a possibility to automate the attribute data acquisition and classification. Thus, we focus on an integration of LiDAR and SAR data to achieve a frequent map update with attribute data acquisition. In this study, we use airborne LiDAR and satellite SAR data to classify buildings. Firstly, we generate a digital surface model (DSM) from point cloud acquired with airborne LiDAR. Secondary, the DSM is registered with a normalized radar cross section (NRCS) image calculated from SAR data. Thirdly, buildings are extracted from the DSM. Finally, the buildings are classified into several clusters in the DSM. We clarified that a combination of airborne LiDAR and satellite SAR data can extract and classify buildings in urban area.

Original languageEnglish
Title of host publication35th Asian Conference on Remote Sensing 2014, ACRS 2014: Sensing for Reintegration of Societies
PublisherAsian Association on Remote Sensing
Publication statusPublished - 2014
Event35th Asian Conference on Remote Sensing 2014: Sensing for Reintegration of Societies, ACRS 2014 - Nay Pyi Taw, Myanmar
Duration: 2014 Oct 272014 Oct 31

Other

Other35th Asian Conference on Remote Sensing 2014: Sensing for Reintegration of Societies, ACRS 2014
CountryMyanmar
CityNay Pyi Taw
Period14/10/2714/10/31

Keywords

  • Airborne LiDAR
  • Building classification
  • Satellite SAR
  • Urban mapping

ASJC Scopus subject areas

  • Computer Networks and Communications

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  • Cite this

    Yamamoto, T., & Nakagawa, M. (2014). Building classification using airborne lidar data with satellite SAR data. In 35th Asian Conference on Remote Sensing 2014, ACRS 2014: Sensing for Reintegration of Societies Asian Association on Remote Sensing.