Classification of building attributes in dense urban areas using ALOS-2 data and airborne LiDAR data

Tatsuya Yamamoto, Masafumi Nakagawa

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

Abstract

In this study, we propose the integration of airborne LiDAR and satellite SAR data for building extraction and classification in four steps. First, we generated a digital surface model (DSM) from airborne LiDAR data. Second, the DSM was registered with a normalized radar cross-section (NRCS) image calculated from the SAR data. Third, buildings were extracted from the DSM, and finally, the buildings were classified into several clusters using NRCS values in the DSM. In our experiment, we selected a dense urban area in Tokyo as our study area. Then, we prepared ALOS-2 PALSAR-2 data and a DSM generated from an airborne LiDAR data. In the building extraction step, we extracted 1778 building roof segments from the DSM. In the classification step, we classified NRCS values of ascending and descending orbit data into several clusters based on ISODATA clustering to estimate building attributes. We conducted an experiment to validate our approach and clarified that a combination of airborne LiDAR and satellite SAR data could extract and classify buildings in a dense urban area.

Original languageEnglish
Title of host publicationACRS 2015 - 36th Asian Conference on Remote Sensing: Fostering Resilient Growth in Asia, Proceedings
PublisherAsian Association on Remote Sensing
Publication statusPublished - 2015
Event36th Asian Conference on Remote Sensing: Fostering Resilient Growth in Asia, ACRS 2015 - Quezon City, Metro Manila, Philippines
Duration: 2015 Oct 242015 Oct 28

Other

Other36th Asian Conference on Remote Sensing: Fostering Resilient Growth in Asia, ACRS 2015
CountryPhilippines
CityQuezon City, Metro Manila
Period15/10/2415/10/28

Fingerprint

Radar cross section
Satellites
Roofs
Orbits
Experiments

Keywords

  • Data fusion and data mining
  • High resolution satellite mapping
  • Urban change monitoring

ASJC Scopus subject areas

  • Computer Networks and Communications

Cite this

Yamamoto, T., & Nakagawa, M. (2015). Classification of building attributes in dense urban areas using ALOS-2 data and airborne LiDAR data. In ACRS 2015 - 36th Asian Conference on Remote Sensing: Fostering Resilient Growth in Asia, Proceedings Asian Association on Remote Sensing.

Classification of building attributes in dense urban areas using ALOS-2 data and airborne LiDAR data. / Yamamoto, Tatsuya; Nakagawa, Masafumi.

ACRS 2015 - 36th Asian Conference on Remote Sensing: Fostering Resilient Growth in Asia, Proceedings. Asian Association on Remote Sensing, 2015.

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

Yamamoto, T & Nakagawa, M 2015, Classification of building attributes in dense urban areas using ALOS-2 data and airborne LiDAR data. in ACRS 2015 - 36th Asian Conference on Remote Sensing: Fostering Resilient Growth in Asia, Proceedings. Asian Association on Remote Sensing, 36th Asian Conference on Remote Sensing: Fostering Resilient Growth in Asia, ACRS 2015, Quezon City, Metro Manila, Philippines, 15/10/24.
Yamamoto T, Nakagawa M. Classification of building attributes in dense urban areas using ALOS-2 data and airborne LiDAR data. In ACRS 2015 - 36th Asian Conference on Remote Sensing: Fostering Resilient Growth in Asia, Proceedings. Asian Association on Remote Sensing. 2015
Yamamoto, Tatsuya ; Nakagawa, Masafumi. / Classification of building attributes in dense urban areas using ALOS-2 data and airborne LiDAR data. ACRS 2015 - 36th Asian Conference on Remote Sensing: Fostering Resilient Growth in Asia, Proceedings. Asian Association on Remote Sensing, 2015.
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