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.

Date Published: 8 November 2012
PDF: 9 pages
Proc. SPIE 8537, Image and Signal Processing for Remote Sensing XVIII, 85370T (8 November 2012); doi: 10.1117/12.974443

Published in SPIE Proceedings Vol. 8537:
Image and Signal Processing for Remote Sensing XVIII
Lorenzo Bruzzone, Editor(s)