Uydu tabanlı arazi kullanımının sınıflandırılması için farklı denetimli yöntemlerin karşılaştırmalı analizi: Reyhanlı örneği

Abstract

Uydu tabanlı arazi kullanımının sınıflandırılması, çevre izlemesinden afet yönetimine kadar çeşitli yeryüzü gözlem uygulamalarında hayati bir rol oynamaktadır. Bu çalışma, Türkiye’nin güneyindeki Reyhanlı ilçesinde, Landsat-9 ve Sentinel-2 uydu görüntülerine uygulanan makine öğrenimi tekniklerinin arazi örtüsü sınıflandırması için karşılaştırmalı bir analizini sunmaktadır. Rastgele Orman (RO), Destek Vektör Makinesi (DVM) ve Maksimum Olabilirlik Sınıflandırması (MOS) olmak üzere üç farklı sınıflandırma algoritması, farklı arazi örtüsü sınıflarını ayırt etme başarısı açısından değerlendirilmiştir. Çalışmada, Coğrafi Bilgi Sistemleri (CBS) yazılımı kullanılarak eşit koşullarda işlenmiş yüksek çözünürlüklü multispektral uydu görüntüleri kullanılmıştır. Görsel inceleme ve istatistiksel değerlendirme, genel doğruluk ve kappa katsayısı da dahil olmak üzere sınıflandırma performansını değerlendirmek için kullanılmıştır. Sentinel-2 ve Landsat-9 uydu görüntülerinin farklı makine öğrenmesi algortimaları ile sınıflandırılması sonucunda en yüksek genel doğruluğun (GD = 0.911, Kappa = 0.879) Sentinel-2 görüntüleri için RF algoritması ile elde edildiği tespit edilmiştir. Bulgular, uydu görüntüsü seçiminin ve algoritma optimizasyonunun doğru arazi örtüsü haritalaması için önemini vurgulamaktadır. Bu çalışma, yerel planlamacılar ve otoriteler için önemli bir bakış açısı sunmakta ve Sentinel-2 görüntüleri ile makine öğrenme tekniklerinin etkili arazi kullanımı, sınıflandırması ve izlenmesi için potansiyelini ortaya koymaktadır.

Keywords

• Coğrafik bilgi sistemleri
• Arazi kullanım sınıflandırması
• Makine öğrenmesi
• Sentinel-2
• Landsat-9

Comparative analysis of different supervised methods for satellite-based land-use classification: A case study of Reyhanlı

Abstract

Satellite-based land-use classification plays a crucial role in various Earth observation applications, ranging from environmental monitoring to disaster management. This study presents a comparative analysis of machine learning techniques applied to land cover classification using Landsat-9 and Sentinel-2 satellite imagery in the Reyhanlı district in southern Türkiye. Three different classification algorithms, Random Forest (RF), Support Vector Machine (SVM), and Maximum Likelihood Classification (MLC), were evaluated for their ability to distinguish different land cover classes. High resolution multispectral satellite imagery processed under the same conditions using Geographic Information System (GIS) software was utilized in this study. Visual inspection and statistical evaluation, including overall accuracy and kappa coefficient, were employed to assess classification performance. The classification of Sentinel-2 and Landsat-9 satellite imagery using different machine learning algorithms resulted in the highest overall accuracy (OA = 0.911, Kappa = 0.879) for Sentinel 2 imagery with the RF algorithm. These findings highlight the importance of satellite image selection and algorithm optimization for accurate land cover mapping. This study provides valuable insights for local planners and authorities and underscores the potential of Sentinel-2 imagery combined with machine learning techniques for effective land-use classification and monitoring.

Keywords

• Geographical information systems
• Land-use classification
• Machine learning
• Sentinel-2
• Landsat-9

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