Sentinel-1A SAR Görüntüsü ile Arazi Örtüsü ve Kullanimi Siniflandirmasi: Istanbul Örneǧi

Mustafa Ustuner; Fusun Balik Sanli; Gokhan Bilgin; Saygin Abdikan

doi:10.1109/SIU.2017.7960373

Sentinel-1A SAR Görüntüsü ile Arazi Örtüsü ve Kullanimi Siniflandirmasi: Istanbul Örneǧi

Translated title of the contribution: Land use and cover classification of Sentinel-IA SAR imagery: A case study of Istanbul

Mustafa Ustuner
, Fusun Balik Sanli
, Gokhan Bilgin
, Saygin Abdikan

Yildiz Technical University
Zonguldak Bülent Ecevit University

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

4 Citations (Scopus)

Abstract

In this study, Sentinel-1A SAR imagery for land use/cover classification and its impacts on classification algorithms were addressed. Sentinel-1A imagery has dual polarization (VV and VH) and freely available from ESA. Istanbul was selected as the study region. After the pre-processing steps including the applying the precise orbit file, calibration, multilooking, speckle filtering and terrain correction, the imagery was classified as the following step. Three classification algorithms (SVM, RF and K-NN) were implemented and the impacts of additional bands (VV-VH, VV+VH etc.) were investigated. Results demonstrated that highest classification accuracy of this study was obtained by SVM classification with the original bands (VV and VH) of Sentinel-1A imagery. Moreover, it was concluded that additional bands had different impacts on each classifier within accuracy.

Translated title of the contribution	Land use and cover classification of Sentinel-IA SAR imagery: A case study of Istanbul
Original language	Turkish
Title of host publication	2017 25th Signal Processing and Communications Applications Conference, SIU 2017
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781509064946
DOIs	https://doi.org/10.1109/SIU.2017.7960373
Publication status	Published - 27 Jun 2017
Externally published	Yes
Event	25th Signal Processing and Communications Applications Conference, SIU 2017 - Antalya, Turkey Duration: 15 May 2017 → 18 May 2017

Publication series

Name	2017 25th Signal Processing and Communications Applications Conference, SIU 2017

Conference

Conference	25th Signal Processing and Communications Applications Conference, SIU 2017
Country/Territory	Turkey
City	Antalya
Period	15/05/17 → 18/05/17

UN SDGs

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

SDG 15 Life on Land

Access to Document

10.1109/SIU.2017.7960373

Cite this

Ustuner, M., Sanli, F. B., Bilgin, G., & Abdikan, S. (2017). Sentinel-1A SAR Görüntüsü ile Arazi Örtüsü ve Kullanimi Siniflandirmasi: Istanbul Örneǧi. In 2017 25th Signal Processing and Communications Applications Conference, SIU 2017 Article 7960373 (2017 25th Signal Processing and Communications Applications Conference, SIU 2017). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SIU.2017.7960373

Ustuner, Mustafa ; Sanli, Fusun Balik ; Bilgin, Gokhan et al. / Sentinel-1A SAR Görüntüsü ile Arazi Örtüsü ve Kullanimi Siniflandirmasi : Istanbul Örneǧi. 2017 25th Signal Processing and Communications Applications Conference, SIU 2017. Institute of Electrical and Electronics Engineers Inc., 2017. (2017 25th Signal Processing and Communications Applications Conference, SIU 2017).

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title = "Sentinel-1A SAR G{\"o}r{\"u}nt{\"u}s{\"u} ile Arazi {\"O}rt{\"u}s{\"u} ve Kullanimi Siniflandirmasi: Istanbul {\"O}rneǧi",

abstract = "In this study, Sentinel-1A SAR imagery for land use/cover classification and its impacts on classification algorithms were addressed. Sentinel-1A imagery has dual polarization (VV and VH) and freely available from ESA. Istanbul was selected as the study region. After the pre-processing steps including the applying the precise orbit file, calibration, multilooking, speckle filtering and terrain correction, the imagery was classified as the following step. Three classification algorithms (SVM, RF and K-NN) were implemented and the impacts of additional bands (VV-VH, VV+VH etc.) were investigated. Results demonstrated that highest classification accuracy of this study was obtained by SVM classification with the original bands (VV and VH) of Sentinel-1A imagery. Moreover, it was concluded that additional bands had different impacts on each classifier within accuracy.",

keywords = "Sentinel 1-A, Synthetic aperture radar, classification, random forest, support vector machines",

author = "Mustafa Ustuner and Sanli, \{Fusun Balik\} and Gokhan Bilgin and Saygin Abdikan",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 25th Signal Processing and Communications Applications Conference, SIU 2017 ; Conference date: 15-05-2017 Through 18-05-2017",

year = "2017",

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Ustuner, M, Sanli, FB, Bilgin, G & Abdikan, S 2017, Sentinel-1A SAR Görüntüsü ile Arazi Örtüsü ve Kullanimi Siniflandirmasi: Istanbul Örneǧi. in 2017 25th Signal Processing and Communications Applications Conference, SIU 2017., 7960373, 2017 25th Signal Processing and Communications Applications Conference, SIU 2017, Institute of Electrical and Electronics Engineers Inc., 25th Signal Processing and Communications Applications Conference, SIU 2017, Antalya, Turkey, 15/05/17. https://doi.org/10.1109/SIU.2017.7960373

Sentinel-1A SAR Görüntüsü ile Arazi Örtüsü ve Kullanimi Siniflandirmasi: Istanbul Örneǧi. / Ustuner, Mustafa; Sanli, Fusun Balik; Bilgin, Gokhan et al.
2017 25th Signal Processing and Communications Applications Conference, SIU 2017. Institute of Electrical and Electronics Engineers Inc., 2017. 7960373 (2017 25th Signal Processing and Communications Applications Conference, SIU 2017).

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

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T2 - 25th Signal Processing and Communications Applications Conference, SIU 2017

AU - Ustuner, Mustafa

AU - Sanli, Fusun Balik

AU - Bilgin, Gokhan

AU - Abdikan, Saygin

PY - 2017/6/27

Y1 - 2017/6/27

N2 - In this study, Sentinel-1A SAR imagery for land use/cover classification and its impacts on classification algorithms were addressed. Sentinel-1A imagery has dual polarization (VV and VH) and freely available from ESA. Istanbul was selected as the study region. After the pre-processing steps including the applying the precise orbit file, calibration, multilooking, speckle filtering and terrain correction, the imagery was classified as the following step. Three classification algorithms (SVM, RF and K-NN) were implemented and the impacts of additional bands (VV-VH, VV+VH etc.) were investigated. Results demonstrated that highest classification accuracy of this study was obtained by SVM classification with the original bands (VV and VH) of Sentinel-1A imagery. Moreover, it was concluded that additional bands had different impacts on each classifier within accuracy.

AB - In this study, Sentinel-1A SAR imagery for land use/cover classification and its impacts on classification algorithms were addressed. Sentinel-1A imagery has dual polarization (VV and VH) and freely available from ESA. Istanbul was selected as the study region. After the pre-processing steps including the applying the precise orbit file, calibration, multilooking, speckle filtering and terrain correction, the imagery was classified as the following step. Three classification algorithms (SVM, RF and K-NN) were implemented and the impacts of additional bands (VV-VH, VV+VH etc.) were investigated. Results demonstrated that highest classification accuracy of this study was obtained by SVM classification with the original bands (VV and VH) of Sentinel-1A imagery. Moreover, it was concluded that additional bands had different impacts on each classifier within accuracy.

KW - Sentinel 1-A

KW - Synthetic aperture radar

KW - classification

KW - random forest

KW - support vector machines

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M3 - Konferans katkısı

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BT - 2017 25th Signal Processing and Communications Applications Conference, SIU 2017

PB - Institute of Electrical and Electronics Engineers Inc.

Y2 - 15 May 2017 through 18 May 2017

ER -

Ustuner M, Sanli FB, Bilgin G, Abdikan S. Sentinel-1A SAR Görüntüsü ile Arazi Örtüsü ve Kullanimi Siniflandirmasi: Istanbul Örneǧi. In 2017 25th Signal Processing and Communications Applications Conference, SIU 2017. Institute of Electrical and Electronics Engineers Inc. 2017. 7960373. (2017 25th Signal Processing and Communications Applications Conference, SIU 2017). doi: 10.1109/SIU.2017.7960373

Sentinel-1A SAR Görüntüsü ile Arazi Örtüsü ve Kullanimi Siniflandirmasi: Istanbul Örneǧi

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UN SDGs

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