Investigation of the performance of classifiers in the extraction of water body areas using Sentinel-2 images

Yıl 2022, Cilt: 12 Sayı: 1, 235 - 245, 15.01.2022

Mehmet Akif Günen

https://doi.org/10.17714/gumusfenbil.992432

Öz

The mapping of water body areas such as rivers, streams, lakes and ponds is very important in terms of monitoring water resources, determining, and managing their change over time. Extracting water body areas is a complicated process that is influenced by a variety of factors. For the problem of identifying water and non-water areas, various multi spectral band satellite imagery and classification-based approaches are used. In this study, non-parametric (Support Vector Machines, k-Nearest Neighborhood and Decision Trees), probabilistic (Hidden Markov Model) and deep learning (Auto-Encoder) based supervised classification, which produce more successful results than index-based methods, were used to investigate the effectiveness of classification algorithms. Since multispectral high spatial resolution satellite images are costly, the water surface areas of Arıklar and Denizli ponds were determined by using only the red, green and blue bands of the Sentinel-2 satellite image for classification. Experimental results were compared using metrics obtained from the confusion matrix such as accuracy, specificity, precision, sensitivity, f-Score, and statistical tools such as mean square error, structural similarity index, peak signal-to-noise ratio, and Kohen's Kappa metric used in image quality determination. In the quantitative and qualitative experimental results obtained, while the deep learning-based auto-encoder was the most successful method statistically, it was determined that the decision trees method worked faster in terms of time comparison.

Anahtar Kelimeler

Image classification, Sentinel-2, Classification, Water body extraction

Sentinel-2 görüntüleri kullanılarak su yüzey alanlarının belirlenmesinde sınıflandırıcıların performanslarının incelenmesi

Öz

Nehirler, akarsular, göller ve göletler gibi su yapılarının haritalanması, su kaynaklarının gözlenmesi, zaman içerisinde değişiminin belirlenmesi ve yönetilmesi açısından oldukça önemlidir. Su yüzey alanlarının tespit edilmesi birçok faktörden etkilenen karmaşık bir süreçtir. Su ve su olmayan alanların belirlenmesi problemi için çeşitli çok kanallı bantlı uydu görüntüleri ve sınıflandırma tabanlı yaklaşımlar kullanılmaktadır. Bu çalışmada, sınıflandırma algoritmalarının etkinliğinin araştırılmasında indeks tabanlı yöntemlerden daha başarı sonuç üreten parametrik olmayan (Destek Vektör Makinalar, k-En Yakın Komşuluk ve Karar Ağaçları), olasılıksal (Saklı Markov Model) ve derin öğrenme (Oto-Kodlayıcı) tabanlı danışmalı sınıflandırma yöntemleri kullanılmıştır. Çok bantlı ve yüksek mekansal çözünürlüklü uydu görüntüleri yüksek maliyetli olduğundan sınıflandırma için Sentinel-2 uydu görüntüsüne ait sadece kırmızı, yeşil ve mavi bantlar kullanılarak Arıklar ve Denizli Göletlerine ait su yüzey alanları belirlenmiştir. Deneysel sonuçlar doğruluk, özgüllük, kesinlik, duyarlılık, f-skor gibi karışıklık matrisinden elde edilen metrikler ve görüntü kalite belirlemede kullanılan ortalama karesel hata, yapısal benzerlik indeksi, pik sinyal-gürültü oranı ve Kohen’in Kappa metriği gibi istatistiksel araçları kullanılarak karşılaştırılmıştır. Elde edilen nicel ve nitel deneysel sonuçlarda derin öğrenme tabanlı oto-kodlayıcı istatistiksel olarak en başarılı yöntem olurken, zamansal karşılaştırma açısından karar ağaçları yönteminin daha hızlı çalıştığı belirlenmiştir.

Anahtar Kelimeler

Görüntü sınıflandırma, Sentinel-2, Sınıflandırma, Su yüzey alanı belirleme

Kaynakça

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