Integration of SAR and GEOBIA for the analysis of time-series data

D. Amitrano, F. Cecinati, G. Di Martino, A. Iodice, P. P. Mathieu, D. Riccio, G. Ruello

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

Abstract

In this work, we present a new architecture for the analysis multitemporal SAR data combining classic synthetic aperture radar processing and geographical object-based image analysis. The architecture exploits the characteristics of the recently introduced RGB products of the Level-1α and Level-1β families, employing self-organizing map clustering and object-based image analysis aiming at the definition of opportune layers measuring scattering and geometric properties of candidate objects to classify. The obtained results have been compared with those given by literature and turned out to provide high degree of accuracy and negligible false alarms. The discussion is supported by an example concerning small reservoir mapping in semi-arid environment.

Original languageEnglish
Title of host publication2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings
PublisherIEEE
Pages4800-4803
Number of pages4
Volume2018-July
ISBN (Electronic)9781538671504
DOIs
Publication statusPublished - 31 Oct 2018
Event38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Valencia, Spain
Duration: 22 Jul 201827 Jul 2018

Conference

Conference38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018
CountrySpain
CityValencia
Period22/07/1827/07/18

Keywords

  • Classification
  • Multitemporal synthetic aperture radar
  • Object-based image analysis
  • Self-organizing map clustering

ASJC Scopus subject areas

  • Computer Science Applications
  • Earth and Planetary Sciences(all)

Cite this

Amitrano, D., Cecinati, F., Di Martino, G., Iodice, A., Mathieu, P. P., Riccio, D., & Ruello, G. (2018). Integration of SAR and GEOBIA for the analysis of time-series data. In 2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings (Vol. 2018-July, pp. 4800-4803). [8518554] IEEE. https://doi.org/10.1109/IGARSS.2018.8518554