Performance Evaluation of Object-based and Pixel-based Building Detection Algorithms from Very High Spatial Resolution Imagery

This paper reviews and evaluates four building extraction algorithms including two pixel-based and two object-based methods using a diverse set of very high spatial resolution imagery. The applied images are chosen from different places (the cities of Isfahan, Tehran, and Ankara) and different sensors (QuickBird and GeoEye-1), which are diverse in terms of building shape, size, color, height, alignment, brightness, and density. The results indicate that the performance and the reliability of two object-based algorithms are better than pixel-based algorithms; about 10 percent to 15 percent better for the building detection rate and 6 percent to 10 percent better for the reliability rate. However, in some cases, the detection rate of pixel-based algorithms has been greater than 80 percent, which is a satisfactory result. On the other hand, segmentation errors can cause limitations and errors in the object-based algorithms, so that the commission error of object-based algorithms has been higher than pixel-based algorithms in some cases.