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Roads and buildings constitute a significant proportion of urban areas. Considerable amount of research has been done on the road and building extraction from remotely sensed imagery. However, a few of them have been concentrating on using only spectral information. This study presents a comparison between three object-based models for urban features' classification, specifically roads and buildings, from WorldView-2 satellite imagery. The three applied algorithms are support vector machines (SVMs), nearest neighbour (NN) and proposed rule-based system. The results indicated that the proposed rules in this study, despite the spectral complexity of land cover types, performed a satisfactory output with an overall accuracy of 92.92%. The advantages offered by the proposed rules were not provided by other two applied algorithms and it revealed the highest accuracy compared to SVM and NN. The overall accuracy for SVM was 76.76%, which is almost similar to the result achieved by NN (77.3%).

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Visual servoing techniques are widely used in the design of autonomous controllers for various robotic arms performing object manipulation in 3D space. Path planning in image space has been studied widely in order to generate the feasible trajectories for the visual servoing controllers. The main idea of image space path planning is to derive a practical paths for image features while taking into account variety of constraints imposed by the image and the physical environment. However, in many practical cases especially in complex and clutter scene spaces extracting the image features is not robust enough and it demands that the target features not to be occluded by the obstacles, robot's body or the target object itself. In this paper, we present a new image space path planning scheme for markerless 3D target that bypasses the feature extraction requirement. Utilizing artificial potential field method the planner generates the desired end-effector path such that a given target remains inside the image boundaries during the robot manipulation task . Experimental results are presented to determine the 2D and 3D generated path with reasonable accuracy for robotic manipulation scenarios.