## 1 Introduction  
With the development of satellite images to provide finer spatial resolutions, they can provide finer more details in urban mapping [1]. However, considering high spectral variation within the same urban class and low spectral variations between different urban classes, image classification has become a challenging issue [1; 2]. Considering the existence of extreme level of detail in very high resolution urban satellite images (VHRSI), object-based methods are being increasingly employed for image classification since they have a higher resemblance to human interpretation skills and in object-based image analysis, object characteristics such as shape, texture, topological information, and spectral response can also be used [3; 4]. In object-based image classifications, an image is divided into non-overlapping segments which are then assigned to different classes using specific methods; for example, [5] presented a method for object-based image classification using a neural network. They used a kernel called \"cloud base\" function for classification. In [6] rule-based classification of very high resolution images, using cartographic data, was used. In the segmentation step, the seeds were picked in the center of cartographic objects; then, using some contextual rules, they classified the image. Although object-based image analysis is not mature enough to be used in automatic image analysis, it is still very promising [5]. Detecting various image objects such as buildings and roads in VHRSI is quite problematic due to uncertain object borders in VHRSI as well as multiplex resemblance of the segments to different classes. Sometimes even the human eye has difficulty differentiating some image objects. Thus, a significant number of studies have added ancillary data, such as road maps, building maps, _etc._, to the process in order to make the procedure easier [7; 8; 9; 10]. Needless to say, those ancillary data are not always available for other studies. In this project, no ancillary data are used. Another group of articles use fuzzy logic to deal with the mentioned complexity. Fuzzy logic, which is developed by [11], has been used in image classification in several studies [12; 13; 14; 15; 16; 17; 18; 19; 20]. In [12], a fuzzy membership matrix for supervised image classification was used. Introducing partial membership of pixels, mixed pixels could be identified and more accurate classification results could be achieved. In addition, [13] used fuzzy logic for Spot image classification. In [14], fuzzy borders for image segmentation was used. In [15], fuzzy segmentation for object-based image classification was adopted. They used a fuzzy classification method on a segmented image to classify large scale areas such as mining fields and transit sites. In [16], Object-Oriented fuzzy analysis of remote sensing data for GIS-ready information was used. In [17; 18; 19; 20], methods for hierarchical image classification using fuzzy logic were also presented. As can be seen fuzzy logic is a quite popular in satellite image analysis. Considering the uncertainties in image pixels/segments, a fuzzy inference system can be of great help in image classification. However, existing literature still suffers from the lack of the literature with a step-by-step image classification method based on fuzzy logic. In this study, a fuzzy inference system is used for image classification in order to detect urban features such as buildings, roads, and vegetation using the tools provided in eCognition software. In this project, no ancillary data such as building maps or road maps are used. The focus is on establishing fuzzy membership functions for object extraction. Typically, the main concern in high resolution satellite image classification is to differentiate objects like vegetation, roads, buildings, _etc._, especially in urban environments. Vegetation extraction methods are probably among the most straightforward object recognition techniques in remote sensing. The Near Infrared (NIR) band plays a crucial role in this field.