GÖRÜNTÜ VE LIDAR VERİSİNDEN BİNA TESPİTİNDE FARKLI YÖNTEMLER

Bu çalışmada dört farklı bina tespit yöntemi incelenmiştir. Her bir yöntem farklı işlem adımlarına sahiptir ve bina tespiti için farklı üstünlüklere sahiptir. İlk yöntem, çok bantlı sınıflandırma ve sayısal yükseklik modeli filtrelenmesine dayanmaktadır. İkinci yöntem, sayısal arazi modeli üzerinde tespit edilmiş arazi üzeri nesneleri ile NDVI görüntüsünün eğitimsiz sınıflandırması ile ağaç nesnelerinin elemine edilmesi ile binaları tespit etmektedir. Üçüncü yöntem, LIDAR arazi noktaları üzerinde yoğunluk analizi ile arazi üzeri nesnelerin tespiti ve yine NDVI kullanarak binaları tespit etmektedir. Dördüncü yöntem ise tamamen LIDAR noktalarına dayanmaktadır, dikey ve yatay düzlem üzerinde yoğunluk analizi ile bina ve ağaçların birbirlerinden ayrışmasını inceleyerek binalar tespit edilmiştir. Daha sonra, yöntemlerin özelliklerine, avantaj ve dezavantajlarına bağlı olarak sonuçlar bütünleştirilmiş ve sırasıyla %94 ve %92 doğruluk ve tamlık değerlerine ulaşılmıştır.

VARIOUS METHODS TO DETECT BUILDINGS USING IMAGE AND LIDAR DATA

Four different variants of building detection are presented. Each variant has a different workflow and is capable of detecting buildings. The first variant of building detection is based on multispectral classification and DSM filtering. In the second variant, DSM blobs, mainly consisting of buildings and trees, are detected by subtraction of the DTM from the DSM. Then, trees are eliminated using NDVI data, derived from unsupervised ISODATA classification of the multispectral images, while small non-building objects are rejected based on size criteria. The third variant uses the planimetric density of raw LIDAR DTM data to detect the above-ground objects. The fourth variant is like the third one, but uses the vertical density of the raw LIDAR data (all points) to distinguish trees and buildings. To improve the results, a combination of the four variants using set intersections and unions is performed. The combination was empirical, with consideration of the datasets used in each variant and the advantages and disadvantages of each variant. In the evaluation, the combination of the four individual results yields 94% correct detections and an omission error of 12% for Zurich airport dataset.

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