Explore multiple clues for urban images matching

Many well-known existing image matching methods are based on local texture analysis, and consequently have difficulty handling low-textured 3D objects, such as those man-made buildings and road networks in urban scenes. In this paper, we propose our urban images matching method utilizing multiple novel clues. Specifically, we explore robust image features generated by interest regions and edge groups extracted from input urban images. Initial correspondences are established based on our features´ similarity measurement. Cost ratio, combined with global context information, is used to remove outliers. We believe our hybrid approach is more suitable than traditional texture features for such scenes. The proposed method has been tested using real aerial urban photos under diverse viewing conditions. Experiments report that our registration success rate is nearly doubled compared with classical methods such as and.