CO-POLAR SAR data classification as a tool for real time paddy-rice monitoring

Caglar Kucuk; Gulsen Taskin Kaya; Esra Erten

doi:10.1109/IGARSS.2015.7326737

CO-POLAR SAR data classification as a tool for real time paddy-rice monitoring

Department of Geomatics Engineering

Istanbul Technical University
Earthquake Engineering and Disaster Management Institute

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

8 Citations (Scopus)

Abstract

The crop phenology retrieval on precision agriculture has been an important research area with the increasing demand on crops. Remotely sensed Synthetic Aperture Radar (SAR) data provides a simple possibility for automatic monitoring of agricultural fields due to the its inherit all-weather monitoring capability. Most of the studies rely on morphology based modelling of the electromagnetic backscattering which requires Monte Carlo simulations. In this paper, instead of modelling the backscattering of the signals for monitoring the crop fields, a classification scheme was implemented on the data acquired by TerraSAR-X by using the features extracted from backscattering coefficients with the machine learning algorithms which are Support Vector Machines, k-Nearest Neighbor and Regression Tree.

Original language	English
Title of host publication	2015 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2015 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	4141-4144
Number of pages	4
ISBN (Electronic)	9781479979295
DOIs	https://doi.org/10.1109/IGARSS.2015.7326737
Publication status	Published - 10 Nov 2015
Event	2015 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2015 - Milan, Italy Duration: 26 Jul 2015 → 31 Jul 2015

Publication series

Name	International Geoscience and Remote Sensing Symposium (IGARSS)
Volume	2015-November
ISSN (Print)	2153-6996
ISSN (Electronic)	2153-7003

Conference

Conference	2015 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2015
Country/Territory	Italy
City	Milan
Period	26/07/15 → 31/07/15

Bibliographical note

Publisher Copyright:
© 2015 IEEE.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 2 Zero Hunger

Keywords

Precision agriculture
classification
machine learning
synthetic aperture radar (SAR)

Access to Document

10.1109/IGARSS.2015.7326737

Cite this

Kucuk, C., Kaya, G. T., & Erten, E. (2015). CO-POLAR SAR data classification as a tool for real time paddy-rice monitoring. In 2015 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2015 - Proceedings (pp. 4141-4144). Article 7326737 (International Geoscience and Remote Sensing Symposium (IGARSS) ; Vol. 2015-November). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IGARSS.2015.7326737

Kucuk, Caglar ; Kaya, Gulsen Taskin ; Erten, Esra. / CO-POLAR SAR data classification as a tool for real time paddy-rice monitoring. 2015 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2015 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2015. pp. 4141-4144 (International Geoscience and Remote Sensing Symposium (IGARSS) ).

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title = "CO-POLAR SAR data classification as a tool for real time paddy-rice monitoring",

abstract = "The crop phenology retrieval on precision agriculture has been an important research area with the increasing demand on crops. Remotely sensed Synthetic Aperture Radar (SAR) data provides a simple possibility for automatic monitoring of agricultural fields due to the its inherit all-weather monitoring capability. Most of the studies rely on morphology based modelling of the electromagnetic backscattering which requires Monte Carlo simulations. In this paper, instead of modelling the backscattering of the signals for monitoring the crop fields, a classification scheme was implemented on the data acquired by TerraSAR-X by using the features extracted from backscattering coefficients with the machine learning algorithms which are Support Vector Machines, k-Nearest Neighbor and Regression Tree.",

keywords = "Precision agriculture, classification, machine learning, synthetic aperture radar (SAR)",

author = "Caglar Kucuk and Kaya, \{Gulsen Taskin\} and Esra Erten",

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Kucuk, C, Kaya, GT & Erten, E 2015, CO-POLAR SAR data classification as a tool for real time paddy-rice monitoring. in 2015 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2015 - Proceedings., 7326737, International Geoscience and Remote Sensing Symposium (IGARSS) , vol. 2015-November, Institute of Electrical and Electronics Engineers Inc., pp. 4141-4144, 2015 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2015, Milan, Italy, 26/07/15. https://doi.org/10.1109/IGARSS.2015.7326737

CO-POLAR SAR data classification as a tool for real time paddy-rice monitoring. / Kucuk, Caglar; Kaya, Gulsen Taskin ; Erten, Esra.
2015 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2015 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2015. p. 4141-4144 7326737 (International Geoscience and Remote Sensing Symposium (IGARSS) ; Vol. 2015-November).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Kucuk, Caglar

AU - Kaya, Gulsen Taskin

AU - Erten, Esra

PY - 2015/11/10

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N2 - The crop phenology retrieval on precision agriculture has been an important research area with the increasing demand on crops. Remotely sensed Synthetic Aperture Radar (SAR) data provides a simple possibility for automatic monitoring of agricultural fields due to the its inherit all-weather monitoring capability. Most of the studies rely on morphology based modelling of the electromagnetic backscattering which requires Monte Carlo simulations. In this paper, instead of modelling the backscattering of the signals for monitoring the crop fields, a classification scheme was implemented on the data acquired by TerraSAR-X by using the features extracted from backscattering coefficients with the machine learning algorithms which are Support Vector Machines, k-Nearest Neighbor and Regression Tree.

AB - The crop phenology retrieval on precision agriculture has been an important research area with the increasing demand on crops. Remotely sensed Synthetic Aperture Radar (SAR) data provides a simple possibility for automatic monitoring of agricultural fields due to the its inherit all-weather monitoring capability. Most of the studies rely on morphology based modelling of the electromagnetic backscattering which requires Monte Carlo simulations. In this paper, instead of modelling the backscattering of the signals for monitoring the crop fields, a classification scheme was implemented on the data acquired by TerraSAR-X by using the features extracted from backscattering coefficients with the machine learning algorithms which are Support Vector Machines, k-Nearest Neighbor and Regression Tree.

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KW - machine learning

KW - synthetic aperture radar (SAR)

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BT - 2015 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2015 - Proceedings

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Kucuk C, Kaya GT , Erten E. CO-POLAR SAR data classification as a tool for real time paddy-rice monitoring. In 2015 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2015 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2015. p. 4141-4144. 7326737. (International Geoscience and Remote Sensing Symposium (IGARSS) ). doi: 10.1109/IGARSS.2015.7326737

CO-POLAR SAR data classification as a tool for real time paddy-rice monitoring

Abstract

Publication series

Conference

Bibliographical note

UN SDGs

Keywords

Access to Document

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