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

doi:10.1109/IGARSS.2018.8518554

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

Department of Architecture & Civil Engineering

Research output: Chapter or section in a book/report/conference proceeding › Chapter in a published conference proceeding

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 language	English
Title of host publication	2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings
Publisher	IEEE
Pages	4800-4803
Number of pages	4
Volume	2018-July
ISBN (Electronic)	9781538671504
DOIs	https://doi.org/10.1109/IGARSS.2018.8518554
Publication status	Published - 31 Oct 2018
Event	38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Valencia, Spain Duration: 22 Jul 2018 → 27 Jul 2018

Conference

Conference	38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018
Country/Territory	Spain
City	Valencia
Period	22/07/18 → 27/07/18

Keywords

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

ASJC Scopus subject areas

Computer Science Applications
General Earth and Planetary Sciences

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10.1109/IGARSS.2018.8518554

Cite this

Integration of SAR and GEOBIA for the analysis of time-series data. / Amitrano, D.; Cecinati, F.; Di Martino, G. et al.
2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings. Vol. 2018-July IEEE, 2018. p. 4800-4803 8518554.

Research output: Chapter or section in a book/report/conference proceeding › Chapter in a published conference proceeding

Amitrano, D, Cecinati, F, Di Martino, G, Iodice, A, Mathieu, PP, 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, 8518554, IEEE, pp. 4800-4803, 38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018, Valencia, Spain, 22/07/18. https://doi.org/10.1109/IGARSS.2018.8518554

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title = "Integration of SAR and GEOBIA for the analysis of time-series data",

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.",

keywords = "Classification, Multitemporal synthetic aperture radar, Object-based image analysis, Self-organizing map clustering",

author = "D. Amitrano and F. Cecinati and {Di Martino}, G. and A. Iodice and Mathieu, {P. P.} and D. Riccio and G. Ruello",

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doi = "10.1109/IGARSS.2018.8518554",

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AU - Amitrano, D.

AU - Cecinati, F.

AU - Di Martino, G.

AU - Iodice, A.

AU - Mathieu, P. P.

AU - Riccio, D.

AU - Ruello, G.

PY - 2018/10/31

Y1 - 2018/10/31

N2 - 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.

AB - 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.

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KW - Multitemporal synthetic aperture radar

KW - Object-based image analysis

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

Abstract

Conference

Keywords

ASJC Scopus subject areas

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