Effective building detection in complex scenes
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Separation of buildings from trees is a major challenge in automatic building detection. In residential and hilly areas, buildings are often surrounded by dense vegetation. This paper presents a three-step method for effective separation of buildings from trees. Firstly, height and width thresholds are applied to LIDAR data for removing small bushes and trees with small horizontal coverage, respectively. The generation of the building mask, where each black region indicates a void area from which there are no laser returns below the height threshold, also helps in separation of buildings from the nearby trees. Then image entropy and colour information are applied together to remove trees exhibiting high texture. Finally, an innovative rule-based procedure is employed using the edge orientation histogram from the imagery to eliminate the remaining trees. Experimental results show that the algorithm offers high building detection rate in complex scenes which are hilly and densely vegetated.
2013 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)
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Photogrammetry and Remote Sensing