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

Year 2022, Volume: 3 Issue: 1, 1 - 19, 14.03.2022

Müslüm Altun , Mustafa Türker

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

Abstract

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.

Keywords

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

Abstract

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.

Keywords

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

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