Farklı Piksel Tabanlı Sınıflandırma Yöntemlerinin Arazi Kullanımı ve Arazi Örtüsü Belirlemedeki Performansının İncelenmesi

Öz

Fotogrametri ve uzaktan algılama tekniklerinin gelişmesiyle birlikte veri toplama daha kolay hale gelmiştir. Ancak toplanan verilerin büyük olması nedeniyle, veri setinden anlamlı veriler çıkarmak son zamanlarda popüler bir araştırma konusu haline gelmiştir. Günümüzde dijital görüntü işleme tekniklerinin geliştirilmesi, arazi örtüsü arazi kullanımının (LCLU) dijital görüntülerle belirlenmesine katkıda bulunmuştur. Bu çalışmada, bir kampüs alanındaki farklı arazi nesne sınıflarını ayırt etmek için ortofoto görüntü üzerinden denetimli sınıflandırma yapılmıştır. Çalışmanın amacı, en popüler denetimli sınıflandırma yöntemlerinden Maksimum Olabilirlik (Maximum Likelihood), Minimum Mesafe (Minimum Distance) ve Mahalanobis Uzaklık (Mahalanobis Distance) sınıflandırma tekniğinin performansını incelemektir. Çalışmada, bir karışıklık matrisi (confusion matrix) oluşturulmuş ve manuel olarak oluşturulan kesin referans verileri ile genel doğruluk ve genel kappa değerleri hesaplanmıştır. Sonuçlara göre, en yüksek genel doğruluk %84,5 oranı ile Maksimum Olabilirlik sınıflandırmasında elde edilmiştir. Minimum Mesafe yöntemi ise en düşük genel doğruluğa (%43) sahiptir. Araştırma, spektral bilgi eksikliğinden dolayı denetimli sınıflandırma yöntemlerinin atlama ve atama hataları (omission and commission) gösterdiğini göstermektedir. Bu durum, genel doğruluk hesaplaması üzerinde doğrudan bir etkiye sahiptir.

Anahtar Kelimeler

• Fotogrametri
• Arazi örtüsü arazi kullanımı
• Denetimli sınıflandırma
• İnsansız hava araçları

Investigation of the Performance of Different Pixel-Based Classification Methods in Land Use/Land Cover (LULC) Determination

Abstract

With the development of photogrammetry and remote sensing techniques, data collection has become easier. However, due to the large size of the data collected, extracting meaningful data from the data set has become a popular topic. Nowadays, the development of digital image processing techniques has contributed to the determination of land cover land use (LCLU) through digital images. In this study, a supervised classification was made over the orthophoto view to distinguish different land object classes in a campus area. The purpose of the study is to examine the performance of the three popular supervised classification techniques that are maximum likelihood, minimum distance, and mahalanobis distance methods. In the study, a confusion matrix was produced, and overall accuracy and overall kappa were calculated with manually generated ground truth data. According to results, the highest overall accuracy was calculated for maximum likelihood classification with a rate of 84.5 % and the minimum distance method has the lowest overall accuracy (43%). The research denotes that due to the lack of spectral information the supervised classification methods generate omission and commission errors. This fact has a direct effect on overall accuracy calculation.

Keywords

• Photogrammetry
• Land cover land use
• Supervised classification
• Unmanned Aerial Vehicles

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