<!-- /* Style Definitions */ p.MsoNormal, li.MsoNormal, div.MsoNormal {mso-style-parent:""; margin:0cm; margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:14.0pt; mso-bidi-font-size:10.0pt; font-family:Arial; mso-fareast-font-family:"Times New Roman"; mso-bidi-font-family:"Times New Roman"; mso-fareast-language:EN-US;} @page Section1 {size:612.0pt 792.0pt; margin:70.85pt 70.85pt 70.85pt 70.85pt; mso-header-margin:35.4pt; mso-footer-margin:35.4pt; mso-paper-source:0;} div.Section1 {page:Section1;} --> Bu çalışmada; dijital hava fotoğraflarından çizgisel ve alansal detayların sınırlarının ve merkez hatlarının yarı otomatik olarak belirlenmesini sağlayan bir yöntem ve bu yöntemin uygulamaya konmasına yönelik bir yazılım geliştirilmiştir. Geliştirilen yöntem, görüntü bölümleme ve düzey kümesi algoritmalarının birlikte kullanılmasına dayanmaktadır Yöntemin uygulanabilirliğinin araştırılması amacıyla 1:35000 ölçekli siyah beyaz hava fotoğrafı üzerinde yarı otomatik detay çizme işlemleri gerçekleştirilmiştir. Bununla birlikte; İTÜ Ayazağa Kampüsünü içeren renkli ortofoto görüntüler kullanılarak yöntemin doğruluk araştırması yapılmış ve binalarda ± 0.463m, yollarda ise ± 0.663 karesel ortalama hatalar tespit edilmiştir. Yapılan doğruluk araştırması sonucunda, geliştirilen yöntemin, kullanılan dijital hava fotoğrafının ±1 pikselinin boyutuna eşit olan bir hata kriterine sahip olduğu sonucuna ulaşılmıştır. Bununla birlikte; bu yöntemin fotogrametrik harita üretiminde ve CBS için fotogrametrik veri sağlanmasında yeni bir yöntem olarak kullanılabileceği değerlendirilmiştir. Özellikle: Göller, sulu dereler ve binalar gibi homojen yapıdaki detayların sınırlarına ait vektör verilerin toplanmasında çok başarılı ve etkili bir şekilde kullanılabileceği görülmüştür. İstenildiği takdirde, tolerans değerinin uygun olarak belirlenmesiyle, söz konusu detaylar üzerinde gözle ayırt edilemeyen sınıflandırmalar ve bölümlemeler gerçekleştirilebileceği tespit edilmiştir. Kaliteli yolların sınırları ve/veya merkez hatları (kullanılan fotoğrafın ölçeğine ve mekânsal ayırma gücüne bağlı olarak) etkili ve hızlı bir şekilde çizilebileceği, ayrıca kırıklık toleransı değerleri değiştirilerek istenilen kırıklıkta vektör veriler elde edilebileceği sonucuna varılmıştır. Raster veriden vektör veriye dönüşümde hem sınırların hem de merkez hatların kullanılabilmesinin etkinliğe çok katkı sağlayacağı düşünülmektedir. Anahtar Kelimeler: Görüntü bölümleme, düzey kümesi, yarı otomatik, dijital hava fotoğrafı.<!-- /* Style Definitions */ p.MsoNormal, li.MsoNormal, div.MsoNormal {mso-style-parent:""; margin:0cm; margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:14.0pt; mso-bidi-font-size:10.0pt; font-family:Arial; mso-fareast-font-family:"Times New Roman"; mso-bidi-font-family:"Times New Roman"; mso-fareast-language:EN-US;} p.zetmetni, li.zetmetni, div.zetmetni {mso-style-name:"Özet metni"; margin-top:6.0pt; margin-right:0cm; margin-bottom:0cm; margin-left:0cm; margin-bottom:.0001pt; text-align:justify; mso-pagination:widow-orphan; font-size:11.0pt; mso-bidi-font-size:10.0pt; font-family:Arial; mso-fareast-font-family:"Times New Roman"; mso-bidi-font-family:"Times New Roman"; mso-fareast-language:EN-US; font-style:italic; mso-bidi-font-style:normal;} @page Section1 {size:612.0pt 792.0pt; margin:70.85pt 70.85pt 70.85pt 70.85pt; mso-header-margin:35.4pt; mso-footer-margin:35.4pt; mso-paper-source:0;} div.Section1 {page:Section1;} --> Aerial photographs have been evaluated manually by the operators for a long time for the extraction of the vector data. Computer technology and digital image processing technologies have been developed and this development provides to perform these extraction processes automatically or semi-automatically. Automatic feature extraction studies are firstly motivated to carry out the extraction of roads from digital images because roads have characteristic attributes like width, surface type and geometrical shape which can be modelled more easily than the others. The resolution of the images has a very important role in the automatic and semi-automatic extraction of the roads. Most known methods are based on the road tracing and the snakes algorithms. Another method of automatic and semi-automatic feature extraction and classification of images is the image segmentation. In recent years, image segmentation and the front propagation of the segments have been carried out successfully by the Level Set and Fast Marching methods. In this study, a semi-automatic line extraction method, based on the segmentation of the images using color-differences of the pixels and the propagation of fronts by the Level Set algorithms, is developed. An object-oriented application software is also developed to test the capabilities of the developed method. Some semi-automatic feature extraction applications are made by the help of the developed software using a 1:35000 scale black/white aerial photograph for determining the capabilities of this method. Another application with 1:5000 scaled two ortho images which have 0.5m resolution of Ayazağa Campus of İstanbul Technical University. These ortho images are generated from 1:16000 scaled color aerial photographs. In this test area, an accuracy test is also carried out to find the accuracy of the developed method. In this accuracy test, vector data of roads and buildings are collected semi-automatically with the developed software and also manually with an experienced operator. The data collected by the operator are assumed the correct ones and they are compared with the others collected by the software. The accuracy test is carried out in two groups. In the first group, on 422 road check points, measurements are made and the square mean root found as ±0.663m. In the second group, buildings are used and 281 check points are measured and the square mean root of this group is equal to ±0.463m.As the results of the applications and tests, it can be said that the accuracy of this developed method is ±1 pixel size of the used imagery. It can be used correctly for producing maps and collecting vector data for GIS. Especially for lakes, rivers and buildings can be collected very efficiently. Different classifications and segmentations, which an operator’s can not see, can be made also with the adjusting of the tolerance value. Roads which have good quality can be vectorized from their center lines and/or boundaries according to the scale of the image used. Some weak sides of this developed method and software are also found out. Especially on big scale aerial photographs, the obstacles on the features, as trees, cars and shadows, effects the extraction of the features negatively. Effects of this factor are reduced whether the scale of the image gets smaller. If the tolerance value is not be adjusted to the correct values, wrong features can be extracted. When a big size image is used, the software gives back some errors because the size of the arrays is directly proportional to the number of the pixels. The quality, contrast and noise of the image effect the feature extraction process. The surface attributes of the features also effect the success degree of the feature extraction. If the noise and the contrast of the images are eliminated by the image process algorithms like edge detection algorithms and filters as anisothropic diffusion and the blanks that are generated by the obstacles on the feature can be interpolated by the different kinds of interpolation methods, more good results can be achieved by the developed method and the software. Also, for the image segmentation different types of segmentation like snakes, instead of color difference and for big size images pyramid levels can be used to increase the success degree of this method. Keywords: Image segmentation, level set, semi-automatic, digital aerial photograph
