Ebru ERSOY TONYALOĞLU, Nurdan ERDOGAN, Betül ÇAVDAR, Kübra KURTŞAN, Engin NURLU

RAPIDEYE UYDU GÖRÜNTÜSÜ İLE PİKSEL TABANLI VE OBJE TABANLI SINIFLANDIRMALARIN KARŞILAŞTIRILMASI

Çalışmanın amacı, RapidEye uydu görüntüsü üzerinde piksel ve obje-tabanlı sınıflandırma yöntemleri karşılaştırarak, alan kullanım/arazi örtüsü sınıflandırma yöntemlerinin performanslarının peyzaj ve sınıf düzeyinde değerlendirilmesidir. Çalışmada, sınıflandırma yüksek çözünürlüklü RapidEye uydu görüntüsü kullanılarak ERDAS Imagine yazılımı kullanılarak piksel-tabanlı kontrollü sınıflandırma işlemi, e-Cognition yazılımı kullanılarak ise obje-tabanlı en yakın komşuluk kontrollü sınıflandırma işlemi uygulanmıştır. Her iki yöntemde de sınıflama, 8 AKAÖ sınıfına göre yapılmıştır. Tematik AKAÖ haritalarının sınıflandırma doğruluğu, her iki yöntemde farklılık gösterirken, piksel-tabanlı sınıflandırma yönteminin genel sınıflandırma doğruluğu %58.39 ve kappa değeri 0.45, obje-tabanlı sınıflandırma yönteminin genel sınıflandırma doğruluğu 89.58% ve kappa değeri 0.86 olarak hesaplanmıştır. Doğruluk analizleri ve sonuçların karşılaştırmalı değerlendirilmesi, obje-tabanlı sınıflandırma yönteminin AKAÖ haritalarının genel doğruluğunun yanı sıra tematik AKAÖ sınıfları için de daha iyi sonuçlar verdiğini göstermiştir. Piksel-tabanlı yöntem birçok tematik sınıfın eşlenmesinde sorun teşkil etmezken, doğal/yarı doğal AKAÖ sınıfları arasında hatalar ortaya çıkmıştır. Doğruluk oranlarında, kullanıcılar tarafından belirlenen kontrol alanı yer seçimi ve kontrol nokta sayısı gibi parametreler ile ilişkilendirilebilinir. Ancak, obje-tabanlı sınıflandırma yönteminde DEM, NDVI gibi yardımcı verilerin de sınıflandırmaya dahil edilebilmesi, yüksek çözünürlüklü uydu görüntüleri ile AKAÖ sınıflandırmada doğruluk oranını arttırmaktadır.

Anahtar Kelimeler:

Piksel-tabanlı sınıflandırma, Obje-tabanlı sınıflandırma, RapidEye, AKAÖ

COMPARISON OF PIXEL AND OBJECT BASED CLASSIFICATION METHODS ON RAPIDEYE SATELLITE IMAGE

The aim of this study is to evaluate the classification performances of land use/land cover (LULC) classification methods by comparing the results of pixel and object-based classification approaches on RapidEye satellite image. Pixel-based classification was carried out in ERDAS Imagine 10.4 using the Maximum Likelihood-supervised approach, whilst object-based classification was performed in e-Cognition Developer 64 using the nearest neighbour-supervised classification method. A LULC map of eight classes was created in both methods. While the accuracy for thematic LULC classes varied in both methods, the overall accuracy and kappa values of LULC maps for pixel and object-based classification methods were 58.39%-0.45 and 89.58%-0.86, respectively. Accuracy assessments and comparative results showed that object-based classification gives better results for thematic LULC classes as well as the overall accuracy of LULC maps. Even though pixel-based classification method was good at mapping many thematic classes, there were misclassifications between natural/semi-natural LULC classes. These results can be attributed to parameters set by users, such as the number of control points, etc. However, the capacity of object-based classification method to include auxiliary data (e.g. DEM, NDVI) increases the accuracy of LULC maps with high-resolution satellites.

Keywords:

Pixel-based classification, Object-based classification, RapidEye, LULC,

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