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

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

Yildiz Technical University
Zonguldak Bülent Ecevit University

Araştırma sonucu: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › bilirkişi

4 Alıntılar (Scopus)

Özet

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.

Tercüme edilen katkı başlığı	Land use and cover classification of Sentinel-IA SAR imagery: A case study of Istanbul
Orijinal dil	Türkçe
Ana bilgisayar yayını başlığı	2017 25th Signal Processing and Communications Applications Conference, SIU 2017
Yayınlayan	Institute of Electrical and Electronics Engineers Inc.
ISBN (Elektronik)	9781509064946
DOI'lar	https://doi.org/10.1109/SIU.2017.7960373
Yayın durumu	Yayınlandı - 27 Haz 2017
Harici olarak yayınlandı	Evet
Etkinlik	25th Signal Processing and Communications Applications Conference, SIU 2017 - Antalya, !!Turkey Süre: 15 May 2017 → 18 May 2017

Yayın serisi

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

???event.eventtypes.event.conference???

???event.eventtypes.event.conference???	25th Signal Processing and Communications Applications Conference, SIU 2017
Ülke/Bölge	!!Turkey
Şehir	Antalya
Periyot	15/05/17 → 18/05/17

BM SKH

Bu sonuç, aşağıdaki Sürdürülebilir Kalkınma Hedefine/Hedeflerine katkıda bulunur

SKH 15 Karasal Yaşam

Keywords

Sentinel 1-A
Synthetic aperture radar
classification
random forest
support vector machines

Belgeye Erişim

10.1109/SIU.2017.7960373

Diger dosyalar ve linkler

Bundan alıntı yap

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 Makale 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",

month = jun,

day = "27",

doi = "10.1109/SIU.2017.7960373",

language = "T{\"u}rk{\c c}e",

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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).

Araştırma sonucu: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › bilirkişi

TY - GEN

T1 - Sentinel-1A SAR Görüntüsü ile Arazi Örtüsü ve Kullanimi Siniflandirmasi

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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DO - 10.1109/SIU.2017.7960373

M3 - Konferans katkısı

AN - SCOPUS:85026323666

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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

Özet

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BM SKH

Keywords

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