Abstract—Very high resolution satellite images provide valuable information to researchers. Among these, urban area boundaries and building locations play crucial roles. For a human expert, manually extracting this valuable information is tedious. One possible solution to extract this information is using automated techniques. Unfortunately, the solution is not straightforward if standard image processing and pattern recognition techniques are used. Therefore, to detect the urban area and buildings in satellite images, we propose using scale invariant feature transform (SIFT) and graph theoretical tools. SIFT keypoints are powerful in detecting objects under various imaging conditions. However, SIFT is not sufficient for detecting urban areas and buildings alone. Therefore, we formalize the problem in terms of graph theory. In forming the graph, we represent each keypoint as a vertex of the graph. The unary and binary relationship between these vertices (such as spatial distance and intensity values) lead to edges of the graph. Based on this formalism, we extract the urban area using a novel multiple subgraph matching method. Then, we extract separate buildings in the urban area using a novel graph cut method. We form a diverse and representative test set using panchromatic one meter resolution Ikonos imagery. By extensive testings, we report very promising results on automatically detecting urban areas and buildings. Index Terms—SIFT; Multiple subgraph matching; Graph cut; Urban area detection; Building detection.

Abstract—In this paper, a novel recognition-driven variational framework is introduced, towards multiple building extraction from aerial and satellite images. To this end, competing shape priors are considered and building extraction is addressed through an image segmentation approach that involves the use of a datadriven term constrained from the prior models. The proposed framework extend previous approaches towards the integration of multiple shape priors into the level set segmentation. In particular, it estimates the number of buildings as well as their pose from the observed data. Therefore, it can address multiple building extraction from a single optical image, a highly demanding task of fundamental importance in various geoscience and remote sensing applications. Furthermore, it can be easily extended to deal with other remote sensing data through a simple modification of the image term. Very promising experimental results and the performed qualitative and quantitative evaluation demonstrate the potential of our approach. Index Terms—variational methods, recognition, segmentation, level sets, extraction, registration, object detection

Model-free image segmentation approaches for automatic building detection, usually fail to detect accurately building boundaries due to shadows, occlusions and other low level misleading information. In this paper, a novel recognition-driven variational framework is introduced for automatic and accurate multiple building extraction from aerial and satellite images. We aim to solve the problem of inaccurate data-driven segmentation. To this end, multiple shape priors are considered. Segmentation is then addressed through the use of a data-driven approach constrained from the prior models. The proposed framework extend previous approaches towards the integration of shape priors into the level set segmentation. In particular, it allows multiple competing priors and estimates buildings pose and number from the observed single image. Therefore, it can address multiple building extraction from single panchromatic images a highly demanding task of fundamental importance in various geoscience and remote sensing applications. Very promising results demonstrate the potentials of our approach. 1