Comparison and Evaluation of Object-Based and Pixel-Based Analysis of LIDAR Data and Large Scale Aerial Imagery in Urban Area

The information extraction of urban features is important for managers and city planners. In this way, we can use remote sensing data and related methods in order to detection of these features efficiently. In this study, Object-based and pixel-based detection of urban features is done by integration of LIDAR data and large-scale aerial optical images at the level of decision. Pixelbased and object-based analysis is done based on decision tree classification with consideration of shadow and without it. This study demonstrates ability of LIDAR data to solve problems caused by the shadow in urban area. The accuracy of object based method is more than pixel based method in two classifications. In object based analysis over all accuracy in both classifications are similar, but classification with shadow class is better than other method. Over all accuracy of pixel based classification without shadow class is 0.91 which is the highest accuracy in pixel-based analysis.