Navigason haritalarında yol ağı verisinin güncellenmesi için uydu verilerinin kullanılabilirliğinin araştırılması
A study on the usability of satellite images for updating road network data of navigation maps
Yazar: SELİN YAZICI
Danışman: DR. ÖĞR. ÜYESİ UĞUR ALGANCI
İstanbul Teknik Üniversitesi / Fen Bilimleri Enstitüsü / Geomatik Mühendisliği Ana Bilim Dalı / Geomatik Mühendisliği Bilim Dalı
Derece: Yüksek Lisans, 2019
Konu: Jeodezi ve Fotogrametri = Geodesy and Photogrammetry

Özet:
Günümüz teknolojisinde, insanlar daha önce bulunmadıkları bir yere ulaşmak veya ulaşmak istedikleri yer hakkında daha ayrıntılı bilgi almak amaçlı navigasyon sistemlerini sıkça kullanmaktadırlar. Son yıllarda neredeyse bütün insanların sürekli beraberlerinde bulundurdukları akıllı telefonlara entegre olan mobil navigasyon sistemleri ve her gün ulaşım için kullanılan araçlardaki araç navigasyon sistemleri insanlara en kolay şekilde navigasyon hizmeti sunan platformlar haline gelmiştir. Bu sistemlerin, kullanıcıları varmak istedikleri yere doğru bir şekilde yönlendirebilme ve bilgilendirebilmesi için sistemde kullanılan haritaya ait verilerin güncel ve güvenilir olması gerekmektedir.
Navigasyon uygulamalarının günlük hayatta bu kadar sık kullanılması sonucu, bu yüksek lisans tez çalışmasının amacı, navigasyon haritalarının hızlı ve güvenilebilir bir biçimde güncellenmesi için uzaktan algılama yöntemlerinden yararlanarak uydu verisi tabanlı yaklaşımların kullanılabilirliğinin araştırılması olarak belirlenmiştir. Bu tez çalışması kapsamında, uzaktan algılama ile veri üretimi yöntemlerinden olan obje tabanlı ve piksel tabanlı sınıflandırma tekniklerinin, diğer veri üretim yöntemlerine göre daha az maliyetli ve hızlı olması, uydu görüntülerinin dijital ortamda kolay işlenebilir olması gibi olumlu yönlerinden dolayı navigasyon haritalarının yol verisinin güncelleme konusundaki kullanılabilirliği incelenmiştir. 
Tez çalışmasına konu çalışma bölgesi olarak, İstanbul Yeni Havalimanı çevresi seçilmiştir. Bu alan kapsamında gelişmekte ve değişmekte olan bölgelerin varlığı, havalimanlarının navigasyon uygulamaları kullanıcıları açısından en önemli noktalardan biri olması ve çalışma sonucunun yapması hedeflenen katkı bakımından önem arz etmektedir. Tez çalışmasında kullanılan yüksek çözünürlüklü Pléiades 1A orto uydu görüntüsünü, belirlenen teknikleri kullanarak işleyebilmek için ERDAS Imagine, eCognition, ArcMap ve FME gibi farklı yazılımlar kullanılmıştır. Yüksek çözünürlüklü uydu görüntüsü; test çalışmaları için, TomTom navigasyon güncel yol vektör verisi ise; uzaktan algılama yöntemleri kullanılarak uydu görüntülerinden elde edilmiş verilerin doğruluğunu ölçme amaçlı referens veri olarak kullanılmıştır. Çalışmanın amacına yönelik yaklaşımlarla ve yöntemlerle ilgili literatür araştırması sonucu tez çalışmasına başlanmıştır.
Piksel tabanlı sınıflandırma yöntemlerinden kontrollü sınıflandırma işlemi olan En Büyük Benzerlik (Maximum Likelihood) sınıflandırma methodunun ERDAS Imagine yazılımı kullanılarak uygulanmasıyla tez çalışmasında bir sonraki adıma geçilmiştir. Uygulamanın doğruluk analizi, yine ERDAS Imagine yazılımı aracılığıyla gerçekleştirilmiştir.
Çalışma kapsamında, sınıflandırma işlemlerinde sağladığı katkıları ile piksel tabanlı sınıflandırmaya iyi bir alternatif olan obje tabanlı sınıflandırma yöntemi, ikinci uygulama olarak gerçekleştirilmiştir. Obje tabanlı sınıflandırma yöntemlerinin en sık tercih edilen yöntemi olan En Yakın Komşuluk (Nearest Neighbor - NN) sınıflandırma işlemi eCognition yazılımı kullanılarak yapılmıştır. Sınıflandırmanın sonuç ürünü için doğruluk analizi, piksel tabanlı sınıflandırma işleminde üretilen kontrol noktaları kullanılarak ERDAS Imagine yazılımı aracılığıyla yapılmıştır. Sınıflandırma işlemi sonucunda elde edilen sonuç verilerinin genelleştirilmesi ArcMap yazılımı aracılığıyla gerçekleştirilmiştir. FME yazılımı ile sınıflandırma sonucu üretilen poligon verisi, merkez çizgi verisine dönüştürülmüş ve ArcMap programı yardımıyla elde edilen çizgi verisi ile TomTom navigasyon güncel yol vektör verisinde olmayan yollar için, kaç kilometre yol vektör verisi güncellemesinin yapılabileceği hesaplanmıştır.  
Sınıflandırma sonuçları karşılaştırıldığında, obje tabanlı sınıflandırma yönteminin piksel tabanlı sınnıflandırma yöntemine göre daha güvenilir ve sağlıklı veri ürettiği, piksel tabanlı sınıflandırma yönteminin bu çalışma için yeterli olmadığı, gerçekleştirilen uygulamaların sonuçları ile kanıtlanmıştır. Bir diğer sonuç olarak; obje tabanlı sınıflandırma sonuç verisi, referans olarak kullanılan TomTom navigasyon güncel yol vektör verisi ile karşılaştırıldığında, obje tabanlı sınıflandırma işlemleri ile navigasyon sistemlerinde eksik olan yol verilerinin güncellenebileceği kanısına varılmıştır.

Abstract:
In today's technology, people mostly use navigation systems to arrive where they have never been before in or to obtain some detailed information about a place that they would like to go to. Using navigation system could be considered as a neccesity, and this need is increasing especially in big cities and touristic areas day by day. The usage of car and mobile navigation systems which are earnings of rapidly developing technology is becoming widespread, since people have needed to make an effort in order to find some signposts that are not easily accessible and visible.
In recent years, mobile navigation systems which have been integrated into smart phones that almost all people have addicted to and car navigation systems that are using for daily transportation are become platforms that execute navigation plans to people easily.
As a result of frequent usage of navigation applications in daily life, the aim of this thesis study was specified as an investigation for the feasibility of satellite data based approaches by using remote sensing methods for a fast and reliable update of navigation maps. The data of navigation maps which is an application of geographic information systems (GIS), is produced by using different techniques. Although, the phase of data generation is the most important for applications of geographic information systems, it is time-consuming and high cost. The most preferred methods for spatial data generation in GIS are surveying techniques, GPS techniques, remote sensing methods, manual digitization of basemaps and photogrammetric methods. Withing the scope of this thesis, due to the fact that object-based and pixel-based classification which are considered as data production methods of remote sensing techniques have positive aspects such as being cost-efficient, being faster methods according to other data production techniques and also since satellite images can easily processed in digital enviroment, the usability of these techniques were investigated for update the road data of navigation map. Istanbul New Airport and its surronding were chosen as study area for this study. Airports are one of the most crucial places to go for users of navigation systems. Besides, existence of ever-developing and changing areas in the chosen area for this study, is important in terms of the targeted contribution as a result.
With respect of the study, preferred Pléiades 1A ortho pan satellite image was processed by using different softwares. High-resolution Pléiades 1A ortho satellite image was used for test studies; up-to-date road network of TomTom Sales was used as a reference for accuracy assessments of data was produced from satellite image by using remote sensing techniques. The study has been started by doing some literature researches about methods and approaches which will be applied regards to aim of the thesis.
The outcome of the production of Pléiades 1A satellite image which was produced on 8th of May 2019 by ITU Research and Application Center for Satellite Communications and Remote Sensing (CSCRS) with 0.5 m pancromatic spatial resolution production is ortho image. The production of current road network, which was used as a reference data, was carried out by TomTom company. Accuracy analysis for the objects that are acquired as a result of object-based classification process was executed by reference data in order to understand how the result reliable is.
The practice stage of thesis study had been started with pixel based controlled classification process by skipping the image preprocessing step which is the first stage of image classification methods due to the specialities of the used high resolution satellite image. Eight classes was defined when the aim of the study is considered. Those classes were specified as "road, sea, field, construction area, forest, water body, construction and wasteland". After creating classes in accordance with purpose, the most crucial stage of the pixel-based classification method which is one of the supervised classification procedures is the selection of study pixels (samples) which will be defined to specified classes. In conjuction with class definition process, adequate amount of samples were collected per each classes. The Maximum Likelihood Classification (MLC), that is a supervised classification algorithm of pixel-based classification method, was performed by using ERDAS Imagine software. For running accuracy assessment by using ERDAS Imagine program, 120 control points were selected for each classes homogeneously and randomly. After random selection of control points, these points were referenced into classified data and the accuracy of the values for control points were compared with ground truth's values. According to accuracy assesment, it was understood from the results of the studies, pixel-based classification methods is not sufficent for road data production of navigation maps. In other words, the accuracy result illustrated that pixel-based classification methods are not feasible for updating road network of navigation maps in a raliable way.
In the thesis scope, the second part of the study was about performing object-based classification method for data production. Object-based classification method is the most preferred method due to having a great amount of advantages for data generation by using remote sensing techniques. While pixels are being used as main units on pixel-based classification process, the main units are objects for object-based classification method. Accordingly, segmentation step that is the first process of the object-based classification method was performed by applying Multiresolution Segmentation (MRS) algorithm that was developed specially for eCognition sofware in order to combine homogenously distributed pixels which have similar spectral values instead of assigning pixels values directly into the classification. Segmentation transaction defined as obtaining objetcs which have same characteristics and pixel values. The main goal of this process is to simplify the analysis activity by making the image simpler, to convert the values of satellite image into meaningful features and to create purposeful object groups on the image. First of all, segmentation process was performed with various scale, smothness and compactness parameters by using eCognition software. After a number of segmentation experiments were done, 150 for scale parameter, 0.5 for smoothness and 0.7 for compactness were considered as the most pertinent values for the segmentation step. The outcomes of segmantation tests demonstrated that the quality of the objects which are created by applying segmentation step with appropriate parameters affects directly the accuracy of the classification process. In the extent of this study, eight classes were identified as "road, sea, field, construction area, forest, water body, construction and wasteland" for the object-based classification. For the defined classes, samples were selected among the objects that were acquired from the result of the segmentation activity and from objects which their classes are known. Then the segmentation phase was completed and the sample areas were selected, the Nearest Neighbor (NN) classification method that is the most preferred object-based classification process was transferred into practice over eCognition sofware. All classification steps were applied on the chosen satellite image and the classified data was exported as polygons in order to make the extension of the product data proper for ArcMap software. Also the generilisation step was performed by ArcMap. Besides, classified data was exported as a raster data in order to generate accuracy assesment by using ERDAS Imagine programme. The accuracy assessment for the result of object-based classification process was done by using same control points which were produced for accuracy assessment phase of pixel-based classification. Those control poins were referenced in the raster data and then, the control points were compared with the values of ground truth. The result of accuracy analysis showed that the total accuracy was calculated as 85.83 % for object-based classification. Accuracy assessment result of object-based classification illustrated that this method is feasible to produce road network for navigation systems. According to the aim of thesis, the parts of classification outcomes that cover reference road network and the parts which have approximate spectral values with roads, such as runway, were eliminated from classification result product to make a calculation for the remained part of it. The result of the area calculation proved that the navigation maps can be updated by object-based classified data for approximately 1.5 km2 area. Furthermore, after elimination step was done, center-lines of polygons were generated for road class by FME software for length calculation in order to estimate how many kilometers could be updated with object-based classified data. Length calculation was perfomed by ArcMAP software. According to length calculation, result product of object-based classified data could update navigation maps for 160 km.
In conclusion, applying pixel-based classification methods are not as feasible as object-based classification methods for data production of navigation map. Also, when the results of classification methods were compared, it demonstrated that the object-based classification method produces more reliable and accurate data than the pixel-based classification method. As another result, the missing roads on navigation maps can be updated by object-based classified data.