The aim of this study is to develop an automated model to extract buildings from aerial images. The fuzzy k-NN classification method is used to extract the buildings by using color information. Also in the thesis, the advantages of the relevance feedback systems are discussed. The software, BuildingLS, is developed in C#. The model is evaluated in 5 different test areas with more than 700 building.

In this research, nonlinear attitude controllers are designed for a low earth orbit satellite intended to be used in observatory missions. The attitude is represented by the Modified Rodriguez Parameters (MRP) which is a minimal representation providing a fully invertible kinematics. As a difference from the classical satellite models existent in the literature, the model of this work incorporates the dynamics of the reaction wheel (actuator) including a brushless dc motor which is armature controlled. The total model has four group of state vectors which are the attitude, body rates, actuator torque and velocity. The main control approach of this research is developed by utilizing integrator back - stepping which provides a recursive stabilization methodology to the designer. For performance comparison, a second controller based on input output feedback linearization (IOFL) is presented. Both of the approaches produce a torque demand law and this is used for generating a desired reaction wheel velocity command. A reaction wheel controller uses the motor as the actuator and produces the necessary amount of the torque according to the desired wheel velocity command. In addition for the back - stepping based approach, a stability analysis against the external disturbance torques is also provided. Simulations are presented for validating the performance and robustness of the proposed controllers.

Performance improvement of a 3-D configuration reconstruction algorithm using a passive secondary target has been focused in this study. In earlier studies, a theoretical development of the 3-D configuration reconstruction algorithm was achieved and it was implemented by a computer program on a system consisting of an optical bench and a digital imaging system. The passive secondary target used was a circle with two internal spots. In order to use this reconstruction algorithm in autonomous systems, an automatic target recognition algorithm has been developed in this study. Starting from a pre-captured and stored 8-bit gray-level image, the algorithm automatically detects the elliptical image of a circular target and determines its contour in the scene. It was shown that the algorithm can also be used for partially captured elliptical images. Another improvement achieved in this study is the determination of internal camera parameters of the vision system.