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

Pınar Karakuş

doi:10.48123/rsgis.1411380

Araştırma Makalesi

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

Yıl 2024, Cilt: 5 Sayı: 1, 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

Kaynakça

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Aldiansyah, S., & Saputra, R. A. (2023). Comparison of machine learning algorithms for land use and land cover analysis using Google Earth Engine (case study: Wanggu Watershed). International Journal of Remote Sensing and Earth Sciences, 19(2), 197-210.
Ao, Y., Li, H., Zhu, L., Ali, S., & Yang, Z. (2019). The linear random forest algorithm and its advantages in machine learning assisted logging regression modeling. Journal of Petroleum Science and Engineering, 174, 776-789.
Avşar, Ö., & Kurtuluş, B. (2017). Köyceğiz Gölü su ve taban sedimanlarının sıcaklık dağılımı. Jeoloji Mühendisliği Dergisi, 41(2), 117-136. https://doi.org/10.24232/jmd.334546
Bar, S., Parida, B. R., & Pandey, A. C. (2020). Landsat-8 And Sentinel-2 based forest fire burn area mapping using machine learning algorithms on GE cloud platform over Uttarakhand, Western Himalaya. Remote Sensing Applications: Society and Environment, 18, 100324. https://doi.org/10.1016/j.rsase.2020.100324
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Dlamini, M., Adam, E., Chirima, G., & Hamandawana, H. (2021). A remote sensing-based approach to investigate changes in land use and land cover in the lower uMfolozi floodplain system, South Africa. Transactions of the Royal Society of South Africa, 76(1), 13–25. https://doi.org/10.1080/0035919X.2020.1858365
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Object Based Classification in Google Earth Engine Combining SNIC and Machine Learning Methods (Case Study: Lake Köyceğiz)

Yıl 2024, Cilt: 5 Sayı: 1, 125 - 137, 28.03.2024

Pınar Karakuş

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

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

Achanta, R., & Süsstrunk, S. (2017). Superpixels and polygons using simple non-iterative clustering. Proceedings of the 30th IEEE Conference on Computer Vision and Pattern Recognition (pp. 4895–4904). IEEE. https://doi.org/10.1109/CVPR.2017.520
Acharya, T. D., Subedi, A., & Lee, D. H. (2019). Evaluation of machine learning algorithms for surface water extraction in a Landsat 8 Scene of Nepal. Sensors, 19(12), 2769. https://doi.org/10.3390/s19122769
Aldiansyah, S., & Saputra, R. A. (2023). Comparison of machine learning algorithms for land use and land cover analysis using Google Earth Engine (case study: Wanggu Watershed). International Journal of Remote Sensing and Earth Sciences, 19(2), 197-210.
Ao, Y., Li, H., Zhu, L., Ali, S., & Yang, Z. (2019). The linear random forest algorithm and its advantages in machine learning assisted logging regression modeling. Journal of Petroleum Science and Engineering, 174, 776-789.
Avşar, Ö., & Kurtuluş, B. (2017). Köyceğiz Gölü su ve taban sedimanlarının sıcaklık dağılımı. Jeoloji Mühendisliği Dergisi, 41(2), 117-136. https://doi.org/10.24232/jmd.334546
Bar, S., Parida, B. R., & Pandey, A. C. (2020). Landsat-8 And Sentinel-2 based forest fire burn area mapping using machine learning algorithms on GE cloud platform over Uttarakhand, Western Himalaya. Remote Sensing Applications: Society and Environment, 18, 100324. https://doi.org/10.1016/j.rsase.2020.100324
Blaschke, T. (2010). Object based image analysis for remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing, 65, 2–16. https://doi.org/10.1016/j.isprsjprs.2009.06.004
Breiman, L., Friedman, J., Olshen, R. A., & Stone, C. J. (1984). Classification and Regression Trees (1st edition). Chapman and Hall/CRC.
Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5-32.
Breiman, L. (2017). Classification and regression trees. Routledge.
Corcoran, J., Knight, J., Pelletier, K., Rampi, L., & Wang, Y. (2015). The effects of point or polygon based training data on Random Forest classification accuracy of wetlands. Remote Sensing, 7(4), 4002-4025. https://doi.org/10.3390/rs70404002
Dlamini, M., Adam, E., Chirima, G., & Hamandawana, H. (2021). A remote sensing-based approach to investigate changes in land use and land cover in the lower uMfolozi floodplain system, South Africa. Transactions of the Royal Society of South Africa, 76(1), 13–25. https://doi.org/10.1080/0035919X.2020.1858365
European Space Agency. (2023, December 19). Introducing Sentinel-2. https://www.esa.int/Applications/ Observing_the_Earth/Copernicus/Sentinel-2/Introducing_Sentinel-2
Gao, Y., & Mas, J. F. (2008). A Comparison of the performance of pixel-based and object-based classifications over images with various spatial resolutions. Online Journal of Earth Sciences, 2(1), 27-35.
Gašparović, M., & Singh, S. K. (2022). Urban surface water bodies mapping using the automatic k-means based approach and sentinel-2 imagery. Geocarto International, 38(1), 2148757. https://doi.org/10.1080/10106049.2022.2148757
Ghamisi, P., & Hoefle, B. (2017). LiDAR data classification using extinction profiles and a composite kernel support vector machine. IEEE Geoscience and Remote Sensing Letters, 14(5), 659-663.
Google Earth Engine. (2023, December 19). FAQ. https://earthengine.google.com/faq/
Goller.gen.tr. (2024, January 29). Köyceğiz Gölü. https://www.goller.gen.tr/koycegiz-golu.html
Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202, 18–27.
Gxokwe, S., Dube, T., & Mazvimavi, D. (2022). Leveraging Google Earth Engine Platform to characterize and map small seasonal wetlands in the semi-arid environments of South Africa. Science of The Total Environment, 803, 150139. https://doi.org/10.1016/j.scitotenv.2021.150139
Heydarian, M., Doyle, T. E., & Samavi, R. (2022). MLCM: Multi-Label confusion matrix. IEEE Access, 10, 19083-19095. https://doi.org/10.1109/ACCESS.2022.3151048
Congalton, R. G. (1991). A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environment, 37(1), 35-46. https://doi.org/10.1016/0034-4257(91)90048-B
Jayaswal, V., (2020, September 14). Performance metrics: Confusion matrix, Precision, Recall, and F1 Score. Towards Data Science. https://towardsdatascience.com/performance-metrics-confusion-matrix-precision-recall-and-f1-score-a8fe076a2262
Jiang, W., Ni, Y., Pang, Z., Li, X., Ju, H., He, G., Lv, J., Yang, K., Fu, J., & Qin, X. (2021). An effective water body extraction method with new water index for sentinel-2 imagery. Water, 13(12), 1647. https://doi.org/10.3390/w13121647
Jiang, L., Zhou, C., & Li, X. (2023). Sub-Pixel Surface Water Mapping for Heterogeneous Areas from Sentinel-2 Images: A Case Study in the Jinshui Basin, China. Water, 15(8), 1446. https://doi.org/10.3390/w15081446
Jiang, Z., Wen, Y., Zhang, G., & Wu, X. (2022). Water information extraction based on multi-model RF algorithm and Sentinel-2 image data. Sustainability, 14(7), 3797. https://doi.org/10.3390/su14073797
Kaplan, G., & Avdan, U. (2017). Object-based water body extraction model using Sentinel-2 satellite imagery. European Journal of Remote Sensing, 50(1), 137-143.
Kobayashi, N., Tani, H., Wang, X., & Sonobe, R. (2020). Crop classification using spectral indices derived from Sentinel-2A imagery. Journal of Information and Telecommunication, 4(1), 67-90.
Li, H., Zech, J., Ludwig, C., Fendrich, S., Shapiro, A., Schultz, M., & Zipf, A. (2021). Automatic mapping of national surface water with OpenStreetMap and Sentinel-2 MSI data using deep learning. International Journal of Applied Earth Observation and Geoinformation, 104, 102571. https://doi.org/10.1016/j.jag.2021.102571
Li, J., Ma, R., Cao, Z., Xue, K., Xiong, J., Hu, M., & Feng, X. (2022). Satellite Detection of Surface Water Extent: A Review of Methodology. Water, 14(7), 1148. https://doi.org/10.3390/w14071148
Liu, Q., Huang, C., Shi, Z., & Zhang, S. (2020). Probabilistic river water mapping from Landsat-8 using the support vector machine method. Remote Sensing, 12(9), 1374. https://doi.org/10.3390/rs12091374
Liu, Q., Tian, Y., Zhang, L., & Chen, B. (2022). Urban Surface Water Mapping from VHR Images Based on Superpixel Segmentation and Target Detection. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 15, 5339-5356.
Loh, W. Y. (2014). Fifty years of classification and regression trees. International Statistical Review, 82(3), 329-348. https://doi.org/10.1111/insr.12016
Luo, C., Qi, B., Liu, H., Guo, D., Lu, L., Fu, Q., & Shao, Y. (2021). Using time series Sentinel-1 images for object-oriented crop classification in Google Earth Engine. Remote Sensing, 13(4), 561. https://doi.org/10.3390/rs13040561
Mahdianpari, M., Salehi, B., Mohammadimanesh, F., Homayouni, S., & Gill, E. (2019). The first wetland inventory map of newfoundland at a spatial resolution of 10 m using sentinel-1 and sentinel-2 data on the Google Earth Engine cloud computing platform. Remote Sensing, 11(1), 43. https://doi.org/10.3390/rs11010043
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Toplam 60 adet kaynakça vardır.

Ayrıntılar

Birincil Dil	İngilizce
Konular	Fotogrametri ve Uzaktan Algılama
Bölüm	Araştırma Makaleleri
Yazarlar	Pınar Karakuş 0000-0003-3727-7233
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

Kaynak Göster

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

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