The impact of geometric features on the detection of water body from point clouds

Year 2024, , 29 - 44, 15.03.2024

Samed Özdemir , Fevzi Karslı

https://doi.org/10.17714/gumusfenbil.1361716

Abstract

High-resolution remote sensing imagery plays a strategic role in critical applications such as water resource management, water quality monitoring, and emergency responses to natural disasters for the quick and accurate identification and extraction of water bodies. However, traditional water body extraction methods present various challenges, particularly in the selection of image texture and characteristic features. In this study, a methodology is proposed that combines geometric features extracted from point cloud data with spectral information obtained from aerial photographs to more effectively define and extract the boundaries of water bodies. The geometric features generated from three-dimensional (3D) structure tensors are merged with the spectral information produced by the sensor system, and the well-known Random Forest (RF) classifier suitable for high-dimensional data, speed, and resistance to overfitting is used for training in water body detection. The effectiveness of the methodology developed in Matlab has been tested over four different locations in Turkey with varying topographic and vegetative characteristics. When the accuracy analysis of the detected water body boundaries is evaluated through the F-Score, the following were obtained: 85.7% for Study Area-1, 76.6% for Study Area-1 River, 93.7% for Study Area-2, 94.9% for Study Area-3, and 73.6% for Study Area-4. The study demonstrates that the presented methodology is applicable across different spatial scales and sensor types and carries potential for comprehensive uses in environmental and hydrological research.

Keywords

Point cloud, Random forest, Digital elevation model, Classification, Water body extraction

Nokta bulutu verisi ile su kütlesi tespitinde geometrik özniteliklerin etkisi

Abstract

Yüksek çözünürlüklü uzaktan algılama görüntülerinden su kütlelerinin hızlı ve doğru bir şekilde tespit edilmesi ve çıkarılması, su kaynakları yönetimi, su kalitesi izleme, doğal afet acil müdahaleleri gibi kritik uygulama alanlarında stratejik bir öneme sahiptir. Bununla birlikte, geleneksel su kütle çıkarma yöntemleri, özellikle görüntü dokusu ve karakteristik özelliklerin seçilmesi konusunda çeşitli zorluklar sunmaktadır. Bu çalışmada, nokta bulutu verilerinden çıkarılan geometrik öznitelikler ve hava fotoğraflarından alınan spektral bilgileri bir araya getirerek, su kütlelerinin sınırlarının daha etkin bir şekilde tanımlanmasını ve çıkarılmasını sağlayan bir metodoloji önerilmektedir. Üç boyutlu (3B) yapı tensöründen yararlanılarak nokta bulutlarından üretilen geometrik öznitelikler algılayıcı sistemin ürettiği spektral bilgiler ile birleştirilerek, yüksek boyutlu verilere uygunluğu, hızı ve aşırı uyuma direnci ile bilinen Rastgele Orman (RO) sınıflandırıcısı su kütlelerinin tespiti için eğitimde kullanılmıştır. Matlab ortamında geliştirilen metodolojinin etkinliği, Türkiye’de topografik ve bitkisel özellikleri farklı dört farklı lokasyon üzerinde test edilmiştir. Sınıflandırma işlemi ile tespit edilen su kütlesi sınırlarının doğruluk analizi F-Skoru üzerinden değerlendirildiğinde, Çalışma Alanı-1 için: %85.7, Çalışma Alanı-1 Akarsu için %76.6, Çalışma Alanı-2 için %93.7, Çalışma Alanı-3 için %94.9, ve Çalışma Alanı-4 için %73.6, olarak elde edilmiştir. Çalışma, sunulan metodolojinin farklı mekânsal ölçekler ve sensör türleri için uygulanabilir olduğunu ve çevresel ve hidrolojik araştırmalarda geniş kapsamlı kullanımlar için potansiyel taşıdığını ortaya koymaktadır.

Keywords

Nokta bulutu, Rastgele orman, Sayısal yükseklik modeli, Sınıflandırma, Su kütlesi çıkarımı

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