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

Pınar Karakuş

doi:10.48123/rsgis.1411380

EN TR

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

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

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

Kaynakça

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Ayrıntılar

Birincil Dil

İngilizce

Konular

Fotogrametri ve Uzaktan Algılama

Bölüm

Araştırma Makalesi

Yazarlar

Pınar Karakuş ^*
0000-0003-3727-7233
Türkiye

Erken Görünüm Tarihi

24 Mart 2024

Yayımlanma Tarihi

28 Mart 2024

Gönderilme Tarihi

28 Aralık 2023

Kabul Tarihi

17 Mart 2024

Yayımlandığı Sayı

Yıl 2024 Cilt: 5 Sayı: 1

DOI

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

IZ

https://izlik.org/JA59AZ64SJ

APA

Karakuş, P. (2024). Object Based Classification in Google Earth Engine Combining SNIC and Machine Learning Methods (Case Study: Lake Köyceğiz). Türk Uzaktan Algılama ve CBS Dergisi, 5(1), 125-137. https://doi.org/10.48123/rsgis.1411380

AMA

1.Karakuş P. Object Based Classification in Google Earth Engine Combining SNIC and Machine Learning Methods (Case Study: Lake Köyceğiz). Turk J Remote Sens GIS. 2024;5(1):125-137. doi:10.48123/rsgis.1411380

Chicago

Karakuş, Pınar. 2024. “Object Based Classification in Google Earth Engine Combining SNIC and Machine Learning Methods (Case Study: Lake Köyceğiz)”. Türk Uzaktan Algılama ve CBS Dergisi 5 (1): 125-37. https://doi.org/10.48123/rsgis.1411380.

EndNote

Karakuş P (01 Mart 2024) Object Based Classification in Google Earth Engine Combining SNIC and Machine Learning Methods (Case Study: Lake Köyceğiz). Türk Uzaktan Algılama ve CBS Dergisi 5 1 125–137.

IEEE

[1]P. Karakuş, “Object Based Classification in Google Earth Engine Combining SNIC and Machine Learning Methods (Case Study: Lake Köyceğiz)”, Turk J Remote Sens GIS, c. 5, sy 1, ss. 125–137, Mar. 2024, doi: 10.48123/rsgis.1411380.

ISNAD

Karakuş, Pınar. “Object Based Classification in Google Earth Engine Combining SNIC and Machine Learning Methods (Case Study: Lake Köyceğiz)”. Türk Uzaktan Algılama ve CBS Dergisi 5/1 (01 Mart 2024): 125-137. https://doi.org/10.48123/rsgis.1411380.

JAMA

1.Karakuş P. Object Based Classification in Google Earth Engine Combining SNIC and Machine Learning Methods (Case Study: Lake Köyceğiz). Turk J Remote Sens GIS. 2024;5:125–137.

MLA

Karakuş, Pınar. “Object Based Classification in Google Earth Engine Combining SNIC and Machine Learning Methods (Case Study: Lake Köyceğiz)”. Türk Uzaktan Algılama ve CBS Dergisi, c. 5, sy 1, Mart 2024, ss. 125-37, doi:10.48123/rsgis.1411380.

Vancouver

1.Pınar Karakuş. Object Based Classification in Google Earth Engine Combining SNIC and Machine Learning Methods (Case Study: Lake Köyceğiz). Turk J Remote Sens GIS. 01 Mart 2024;5(1):125-37. doi:10.48123/rsgis.1411380

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IEEE Access

https://doi.org/10.1109/ACCESS.2024.3519612

Recent lake surface dynamics in the Hunshandake sandy land (2017–2022) and their response to climatic factors

Ecological Indicators

https://doi.org/10.1016/j.ecolind.2025.113820

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

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

Öz

Anahtar Kelimeler

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

Öz

Anahtar Kelimeler

Kaynakça

Ayrıntılar

Birincil Dil

Konular

Bölüm

Yazarlar

Erken Görünüm Tarihi

Yayımlanma Tarihi

Gönderilme Tarihi

Kabul Tarihi

Yayımlandığı Sayı

DOI

IZ

Kaynak Göster

Cited By

A New Texture Aware—Seed Demand Enhanced Simple Non-Iterative Clustering (ESNIC) Segmentation Algorithm for Efficient Land Use and Land Cover Mapping on Remote Sensing Images

Recent lake surface dynamics in the Hunshandake sandy land (2017–2022) and their response to climatic factors