Kaynaştırılmış Sentinel-1 SAR ve Landsat-8 Optik Veriden Makine Öğrenme Algoritması ile Tarımsal Ürün Tespiti

Müslüm Altun; Mustafa Türker

doi:10.48123/rsgis.999749

EN TR

Agricultural Crop Detection with a Machine Learning Algorithm from Fused Sentinel-1 SAR and Landsat-8 Optical Data

Öz

In this study, the effect of fused single-date Sentinel-1 SAR and Landsat-8 data on agricultural crop detection by classification was investigated. A region in Manisa, Turkey was chosen as study area. Ehlers method was used for image fusion and random forest (RF) machine learning algorithm was used for image classification. Classification was carried out using only Sentinel-1 data, only Landsat-8 data, and the fused Sentinel-1 and Landsat-8 datasets. In classification using only Sentinel-1 VV or VH band, the overall accuracies were calculated at the level of 35%. The combined use of Sentinel-1 VV and VH bands contributed about 6% to classification performance. The accuracy (71,18%) calculated from classification performed using Landsat-8 data alone is quite high when compared to classification performed using Sentinel-1 data alone. In classification with the fused Landsat-8 and Sentinel-1 DD dataset and with the fused Landsat-8 and Sentinel-1 DY dataset, the accuracies were calculated as 80.44% and 82.16%, respectively. The highest accuracy (87.72%) was obtained in classification performed using the fused Landsat-8 and Sentinel-1 VV+VH bands dataset. Based on the results, the use of the fused single-date Landsat-8 and Sentinel-1 VV+ VH bands dataset in classification was found to significantly increase the accuracy.

Anahtar Kelimeler

Sentinel-1 SAR, Landsat 8, Classification, Random Forest (RF), Ehlers Image Fusion

Kaynaştırılmış Sentinel-1 SAR ve Landsat-8 Optik Veriden Makine Öğrenme Algoritması ile Tarımsal Ürün Tespiti

Öz

Bu çalışmada, tek tarihe ait kaynaştırılmış Sentinel-1 Yapay Açıklıklı Radar (Synthetic Aperture Radar-SAR) ve Landsat-8 verilerinin sınıflandırma ile tarımsal ürün tespitine olan etkisi araştırılmıştır. Çalışma alanı olarak, Manisa’da bir bölge seçilmiştir. Görüntü kaynaştırma için Ehlers yöntemi, görüntü sınıflandırma için rastgele orman (RO) makine öğrenme algoritması kullanılmıştır. Sınıflandırma orjinal Sentinel-1 verisi ile orjinal Landsat-8 verisi ile ve kaynaştırılmış veri setleri ile gerçekleştirilmiştir. Orjinal Sentinel-1 DD veya DY bandı ile yapılan sınıflandırmanın genel doğruluğu %35 mertebesinde hesaplanmıştır. Sentinel-1 DD ve DY bantların birlikte kullanılmasının sınıflandırma performansına katkısı %6 kadar olmuştur. Orjinal Landsat-8 verisi ile yapılan sınıflandırma sonucu hesaplanan genel doğruluk değeri (%71,18), orjinal Sentinel-1 verisine göre oldukça yüksektir. Landsat-8 ile kaynaştırılmış Sentinel-1 DY ve DD batları veri setleri ile yapılan sınıflandırmanın genel doğruluğu sırasıyla %80,44 ve %82,16 olarak hesaplanmıştır. En yüksek genel doğruluk değeri (%87,72), Landsat-8 ile kaynaştırılmış Sentinel-1 DD+DY bantları veri seti ile yapılan sınıflandırmada elde edilmiştir. Elde edilen bulgulara göre, sınıflandırmada kaynaştırılmış tek tarihli Landsat-8 ve Sentinel-1 DD+DY bantları veri setinin kullanılması doğruluğu önemli oranda artırmaktadır.

Anahtar Kelimeler

Sentinel-1 SAR, Landsat-8, Sınıflandırma, Rastgele Orman (RO), Ehlers Görüntü Kaynaştırma

Kaynakça

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Ayrıntılar

Birincil Dil

Türkçe

Konular

Fotogrametri ve Uzaktan Algılama

Bölüm

Araştırma Makalesi

Yazarlar

Müslüm Altun ^*
0000-0002-5603-6331
Türkiye

Mustafa Türker
0000-0001-5604-0472
Türkiye

Yayımlanma Tarihi

14 Mart 2022

Gönderilme Tarihi

23 Eylül 2021

Kabul Tarihi

31 Ocak 2022

Yayımlandığı Sayı

Yıl 2022 Cilt: 3 Sayı: 1

DOI

https://doi.org/10.48123/rsgis.999749

IZ

https://izlik.org/JA33JY69ER

APA

Altun, M., & Türker, M. (2022). Kaynaştırılmış Sentinel-1 SAR ve Landsat-8 Optik Veriden Makine Öğrenme Algoritması ile Tarımsal Ürün Tespiti. Türk Uzaktan Algılama ve CBS Dergisi, 3(1), 1-19. https://doi.org/10.48123/rsgis.999749

AMA

1.Altun M, Türker M. Kaynaştırılmış Sentinel-1 SAR ve Landsat-8 Optik Veriden Makine Öğrenme Algoritması ile Tarımsal Ürün Tespiti. Turk J Remote Sens GIS. 2022;3(1):1-19. doi:10.48123/rsgis.999749

Chicago

Altun, Müslüm, ve Mustafa Türker. 2022. “Kaynaştırılmış Sentinel-1 SAR ve Landsat-8 Optik Veriden Makine Öğrenme Algoritması ile Tarımsal Ürün Tespiti”. Türk Uzaktan Algılama ve CBS Dergisi 3 (1): 1-19. https://doi.org/10.48123/rsgis.999749.

EndNote

Altun M, Türker M (01 Mart 2022) Kaynaştırılmış Sentinel-1 SAR ve Landsat-8 Optik Veriden Makine Öğrenme Algoritması ile Tarımsal Ürün Tespiti. Türk Uzaktan Algılama ve CBS Dergisi 3 1 1–19.

IEEE

[1]M. Altun ve M. Türker, “Kaynaştırılmış Sentinel-1 SAR ve Landsat-8 Optik Veriden Makine Öğrenme Algoritması ile Tarımsal Ürün Tespiti”, Turk J Remote Sens GIS, c. 3, sy 1, ss. 1–19, Mar. 2022, doi: 10.48123/rsgis.999749.

ISNAD

Altun, Müslüm - Türker, Mustafa. “Kaynaştırılmış Sentinel-1 SAR ve Landsat-8 Optik Veriden Makine Öğrenme Algoritması ile Tarımsal Ürün Tespiti”. Türk Uzaktan Algılama ve CBS Dergisi 3/1 (01 Mart 2022): 1-19. https://doi.org/10.48123/rsgis.999749.

JAMA

1.Altun M, Türker M. Kaynaştırılmış Sentinel-1 SAR ve Landsat-8 Optik Veriden Makine Öğrenme Algoritması ile Tarımsal Ürün Tespiti. Turk J Remote Sens GIS. 2022;3:1–19.

MLA

Altun, Müslüm, ve Mustafa Türker. “Kaynaştırılmış Sentinel-1 SAR ve Landsat-8 Optik Veriden Makine Öğrenme Algoritması ile Tarımsal Ürün Tespiti”. Türk Uzaktan Algılama ve CBS Dergisi, c. 3, sy 1, Mart 2022, ss. 1-19, doi:10.48123/rsgis.999749.

Vancouver

1.Müslüm Altun, Mustafa Türker. Kaynaştırılmış Sentinel-1 SAR ve Landsat-8 Optik Veriden Makine Öğrenme Algoritması ile Tarımsal Ürün Tespiti. Turk J Remote Sens GIS. 01 Mart 2022;3(1):1-19. doi:10.48123/rsgis.999749

Cited By

Integration of convolutional neural networks with parcel-based image analysis for crop type mapping from time-series images

Earth Science Informatics

https://doi.org/10.1007/s12145-025-01819-8

Turkish Journal of Remote Sensing and GIS (Türk Uzaktan Algılama ve CBS Dergisi), Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License ile lisanlanmıştır.

Agricultural Crop Detection with a Machine Learning Algorithm from Fused Sentinel-1 SAR and Landsat-8 Optical Data

Öz

Anahtar Kelimeler

Kaynaştırılmış Sentinel-1 SAR ve Landsat-8 Optik Veriden Makine Öğrenme Algoritması ile Tarımsal Ürün Tespiti

Öz

Anahtar Kelimeler

Kaynakça

Ayrıntılar

Birincil Dil

Konular

Bölüm

Yazarlar

Yayımlanma Tarihi

Gönderilme Tarihi

Kabul Tarihi

Yayımlandığı Sayı

DOI

IZ

Kaynak Göster

Cited By

Integration of convolutional neural networks with parcel-based image analysis for crop type mapping from time-series images