This paper presents a new method for rooftop extraction that integrates color features, height map, and road information
in a level set based segmentation framework. The proposed method consists of two steps: rooftop detection and rooftop
segmentation. The first step requires the user to provide a few example rooftops from which the color distribution of
rooftop pixels is estimated. For better robustness, we obtain superpixels of the input satellite image, and then classify
each superpixel as rooftop or non-rooftop based on its color features. Using the height map, we can remove those
detected rooftop candidates with small height values. Level set based segmentation of each detected rooftop is then
performed based on color and height information, by incorporating a shape-prior term that allows the evolving contour
to take on the desired rectangle shape. This requires performing rectangle fitting to the evolving contour, which can be
guided by the road information to improve the fitting accuracy. The performance of the proposed method has been
evaluated on a satellite image of 1 km×1 km in area, with a resolution of one meter per pixel. The method achieves
detection rate of 88.0% and false alarm rate of 9.5%. The average Dice's coefficient over 433 detected rooftops is 73.4%.
These results demonstrate that by integrating the height map in rooftop detection and by incorporating road information
and rectangle fitting in a level set based segmentation framework, the proposed method provides an effective and useful
tool for rooftop extraction from satellite images.