Google Earth Engine Platformunda SNIC ve Makine Öğrenimi Yöntemlerini Birleştiren Nesne Tabanlı Sınıflandırma (Örnek Olay: Köyceğiz Gölü)

Yıl 2024, , 125 - 137, 28.03.2024

Pınar Karakuş

https://doi.org/10.48123/rsgis.1411380

Öz

Köyceğiz Gölü, Akdeniz Bölgesi'nin batı ucunda yer alan kükürt bakımından zengin, ülkemizin en kritik kıyı set göllerinden biridir. Dalyan Boğazı ile Akdeniz’e bağlanan Köyceğiz Gölü, bu özelliği ile de dünyadaki 7 gölden birisidir. Bu çalışmada, Nesne Tabanlı Görüntü Sınıflandırma yöntemi, makine öğrenimi algoritmalarından SRA (Sınıflandırma ve Regresyon Ağaçları), RO (Rasgele Orman) ve DVM (Destek Vektör Makineleri) algoritmaları ile bütünleştirerek Köyceğiz gölünün su değişim analizi gerçekleştirilmiştir. Görüntüyü süper piksellere bölerek nesne düzeyinde ayrıntılı bir analize olanak tanıyan Basit Yinelemesiz Kümeleme (BYK) segmentasyon yöntemi kullanılmıştır. Çalışma alanına ait Sentinel 2 Harmonized görüntüleri 2019, 2020, 2021 ve 2022 yılları için Google Earth Engine (GEE) platformundan elde edilmiş ve tüm hesaplamalar GEE’de yapılmıştır. Dört yılın sınıflandırma doğrulukları incelendiğinde BYK algoritması ile SRA, RO ve DVM makine öğrenme algoritmalarının kombinasyonu ile elde edilen nesne tabanlı sınıflandırma yöntemi kullanılarak göl su alanının bütün yöntemler için sınıflandırma doğruluklarının(ÜD, KD, GD ve Kappa) %92'nin üstünde, F-score’un 0.98’in üzerinde olduğu görülmüştür. SVM algoritmasının SRA ve RO yöntemlerine göre göl su alanının belirlenmesinde daha yüksek değerlendirme metriklerine sahip olduğu belirlenmiştir.

Anahtar Kelimeler

GEE, Basit yinelemeli kümeleme, Nesne tabanlı sınıflandırma, Göl yüzey alanı, Sentinel 2, Makine öğrenmesi

Object Based Classification in Google Earth Engine Combining SNIC and Machine Learning Methods (Case Study: Lake Köyceğiz)

Öz

Köyceğiz Lake is one of our country’s most critical coastal barrier lakes, rich in sulfur, located at the western end of the Mediterranean Region. Köyceğiz Lake, connected to the Mediterranean via the Dalyan Strait, is one of the 7 lakes in the world with this feature. In this study, water change analysis of Köyceğiz Lake was carried out by integrating the Object-Based Image Classification method with CART (Classification and Regression Tree), RF (Random Forest), and SVM (Support Vector Machine) algorithms, which are machine learning algorithms. SNIC (Simple Non-iterative Clustering) segmentation method was used, which allows a detailed analysis at the object level by dividing the image into super pixels. Sentinel 2 Harmonized images of the study area were obtained from the Google Earth Engine (GEE) platform for 2019, 2020, 2021, and 2022,and all calculations were made in GEE. When the classification accuracies of four years were examined, it was seen that the classification accuracies(OA, UA, PA, and Kappa) of the lake water area were above 92%, F-score was above 0.98 for all methods using the object-based classification method obtained by the combination of the SNIC algorithm and CART, RF, and SVM machine learning algorithms. It has been determined that the SVM algorithm has higher evaluation metrics in determining the lake water area than the CART and RF methods.

Anahtar Kelimeler

GEE, Simple non-iterative clustering, Object based classification, Lake surface area, Sentinel 2, Machine learning

Kaynakça

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