Abstract
A city mapping is essential to various applications such as planning, transport management, vehicle navigation, intervention in natural disasters, etc…. For convenience and efficiency, such applications are integrated in a Geographical Information System (GIS) (Bendouda and Berrached, in Etude et réalisation d’UREGIS un SIG pour la gestion du réseau routier urbain, Magister Thesis, University of Sciences and Technology of Oran Algeria 2009). GIS Map needs real time automatic updating and revisions of the road network databases. However, due to the extreme complexity of the urban environment, there are currently many methods involving the extraction of roads by means of automatic or semi-automatic approaches in rural and sub-urban areas; but in urban environment the majority of these methods failed, due to the complexity of this environment and the complex appearance of the road in the remotely sensed image. In this paper, we introduce a new approach to extract road network in urban area from low resolution satellite images. The proposed method is a modified version of the dynamic programming method for semi-automatic extraction of road network, based on the F* algorithm. The preliminary step is the seeding of points belonging to roads. F* detects the segments that may belong to a road by optimizing certain criteria. Given the complexity of urban areas and the existence of different road categories, we propose an improved version of the classical algorithm F* called PR-F*(Parallel Research-F*). It detects the road segments automatically in many directions. The obtained results are evaluated in terms of quality with respect to completeness and correctness.
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Anil, P. N., & Natarajan, S. A. (2010). Novel approach using active contour model for semi-automatic road extraction from high resolution satellite imagery. In Second International Conference on Machine Learning and Computing, (pp. 263–266).
Barzohar, M., & Cooper, D. B. (1996). Automatic finding of main roads in aerial images by using geometric-stochastic models and estimation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 18(7), 707–721.
Bendouda, M., & Berrached, N. (2009). Etude et réalisation d’UREGIS un SIG pour la gestion du réseau routier urbain. Magister Thesis, University of Sciences and Technology of Oran, Algeria.
Bonnefon, R. (2002). Extraction d’objets cartographiques à partir d’images de télédétection: possibilité d’application à la mise à jour de Systèmes d’Information Géographique. Ph.D. Thesis, Université Paul Sabatier Toulouse 3, France.
Chaudhuri, D., Kushwaha, N. K., & Samal, A. (2012). Semi-automated road detection from high resolution satellite image by directional morphological enhancement and segmentation techniques. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 5(5), 1538–1544.
Dal Poza, A. P., Gallisb, R. A., & Silva, J. F. C. (2010). Semi-automatic road extraction by dynamic programming optimisation in the object space: single image case. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Science, 34.
Das, S., Mirnalinee, T. T., & Varghese, K. (2011). Use of salient features for the design of a multistage framework to extract roads from high-resolution multispectral satellite images. IEEE Transactions on Geoscience and Remote Sensing, 49(10), 3906–3931.
Dell’Acqua, F., & Gamba, P. (2006). Improving urban road extractionin high-resolution images exploiting directional filtering, perceptual grouping, and simple topological concepts. IEEE Transactions on Geoscience and Remote Sensing, 3(3), 387–391.
Felzenszwalb, P. F., & Zabih, R. (2011). Dynamic programming and graph algorithms in computer vision. IEEE Transaction on Pattern Analysis and Machine Intelligence, 33(4), 721–740.
Fua, P., & Leclerc, Y. G. (1990). Model driven edge detection. Machine Vision and Application, 3(1), 45–56.
Gruen, A., & Li, H. (1999). Semi-automatic linear feature extraction by dynamic programming and LSB-snakes. Photogrammetric Engineering and Remote Sensing, 63(8), 985–995.
Heipke, C., Grote, A., & Rottensteiner, F. (2012). Road network extraction in suburban areas. The Photogrammetric Record, 27(137), 8–28.
Heipke, C., Mayer, H., & Wiedemann, C. (1997). Evaluation of automatic road extraction. International Archives of Photogrammetry and Remote Sensing, 32(3-2W3), 47–56.
Herumuti, D., Uchimura, K., & Koutaki, G. (2013). Urban road extraction based on Hough transform and region growing. In The 19th Korea-Japan Joint Workshop on Frontiers of Computer Vision, Incheon.
Huang, X., & Zhang, L. (2009). Road center line extraction from high-resolution imagery based on multiscale structural features and support vector machines. International Journal of Remote Sensing, 30(8), 1977–1987.
Lacoste, C. (2004). Extraction de réseaux linéiques à partir d’images satellitaires et aériennes par processus ponctuels marqués. Ph.D. Thesis, Université Nice Sophia Antipolis, France.
Ma, A. R., Wang, W., & Liu, S. (2012). Extracting roads based on Retinex and improved Canny operator with shape criteria in vague and unevenly. Journal of Applied Remote Sensing, 6(1), 063610.
Movaghati, S. (2010). Road extraction from satellite images using particle filtering and extended Kalman filtering. IEEE Transaction on Geoscience and Remote Sensing, 48(7), 2807–2817.
Peteri, R. (2003). Extraction de réseaux de rues en milieu urbain à partir d’images satellites à très haute résolution spatiale. Ph.D. Thesis, Ecole des Mines de Paris, France.
Peteri, R., & Ranchin, T. (2003). Multi-resolution snakes for urban road extraction from Ikonos and Quickbird images. In 23rd European Association of Remote Sensing Laboratories, (pp. 141–147).
Poullis, C., & You, S. (2010). Delineation and geometric modeling of road networks. ISPRS Journal of Photogrammetry and Remote Sensing, 65(2), 165–181.
Shi, W. Z., Miao, Z. L., & Debayle, J. (2014). An integrated method for urban main-road center-line extraction form optical remotely sensed imagery. IEEE Transactions on Geoscience and Remote Sensing, 52(6), 3359–3372.
Treash, K., & Amaratunga, K. (2000). Automatic road detection in gray scale aerial images. ASCE Journal of Computing in Civil Engineering, 14(1), 60–69.
Ünsalan, C., & Sirmacek, B. (2012). Road network detection using probabilistic and graph theoretical methods. IEEE Transactions on Geoscience and Remote Sensing, 50(11), 4441–4453.
Valero, S., Chanussut, J., & Benediktsson, J. A. (2010). Advanced directional mathematical morphology for the detection of the road network in very high resolution remote sensing images. Pattern Recognition Letters, 31(10), 1120–1127.
Wang, W. (2016). A review of road extraction from remote sensing images. Journal of Traffic and Transactions Engineering, 3(3), 271–282.
Wang, J. L., Qian, J. H., & Ma, R. B. (2013). Urban road information extraction from high resolution remotely sensed image based on semantic model. In 21th International Conference on Geoinformatics, Shanghai.
Yuan, J., Wang, D., Wu, B., Yan, L., & Li, R. (2011). Region-based automatic road extraction from satellite imagery. IEEE Transactions on Geoscience and Remote Sensing, 49(11), 4528–4538.
Zhu, C., Shi, W., & Pesaresi, M. (2005). The recognition of road network from high-resolution satellite remotely sensed data using image morphological characteristics. International Journal of Remote Sensing, 26(24), 5493–5508.
Zhu, D. M., Wen, X., & Ling, C. L. (2011). Road extraction based on the algorithms of MRF and hybrid model of SVM and FCM. In International Symposium on Image and Data Fusion (pp. 1–4).
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Bendouda, M., Berrached, N. Urban Road Network Extraction from Remote Sensing Images Using an Improved F* Algorithm. J Indian Soc Remote Sens 46, 1053–1060 (2018). https://doi.org/10.1007/s12524-018-0773-3
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DOI: https://doi.org/10.1007/s12524-018-0773-3