Abstract
In this paper, we present a probabilistic framework for urban area extraction in remote sensing images using a conditional random field built over an adjacency graph of superpixels. Our discriminative model performs a multi-cue combination by incorporating efficiently color, texture and edge cues. Both local and pairwise feature functions are learned using sharing boosting to obtain a powerful classifier based on feature selection. Urban area are accurately extracted in highly heterogenous satellite images by applying a cluster sampling method, the Swendsen-Wang Cut algorithm. Example results are shown on high resolution SPOT-5 satellite images.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Sarkar, A., Banerjee, A., Banerjee, N., Brahma, S., Kartikeyan, B., Chakraborty, M., Majumder, K.L.: Landcover classification in MRF context using Dempster-Shafer fusion for multisensor imagery. IEEE Trans. Image Processing 14, 634–645 (2005)
Kumar, S., Hebert, M.: Man-made structure detection in natural images using a causal multiscale random field. In: IEEE Conf. on Computer Vision and Pattern Recognition, vol. 1, pp. 119–126 (2003)
Li, F., Peng, J., Zheng, X.: Object-Based and Semantic Image Segmentation Using MRF. EURASIP Journal on Applied Signal Processing, 833–840 (2004)
Lafferty, J.D., A.M., Pereira, F.C.N.: Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data. In: International Conference on Machine Learning, pp. 282–289 (2001)
Kumar, S., Hebert, M.: Discriminative Random Fields. International Journal of Computer Vision 68, 179–201 (2006)
Ren, X., Fowlkes, C.C., Malik, J.: Cue integration in figure/ground labeling. Advances in Neural Information Processing Systems 18 (2005)
Torralba, A., Murphy, K.P., Freeman, W.T.: Contextual models for object detection using boosted random fields. Advances in Neural Information Processing Systems 17 (2005)
He, X., Zemel, R.S., Carreira-Perpinan, M.A.: Multiscale Conditional Random Fields for Image Labeling. In: IEEE Conf. on Computer Vision and Pattern Recognition, vol. 2, pp. 695–702 (2004)
Kumar, S., Hebert, M.: A hierarchical field framework for unified context based classification. In: IEEE International Conference on Computer Vision, vol. 2, pp. 1284–1291 (2005)
Shotton, J., Winn, J.M., Rother, C., Criminisi, A.: TextonBoost: Joint Appearance, Shape and Context Modeling for Multi-class Object Recognition and Segmentation. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3951, pp. 1–15. Springer, Heidelberg (2006)
Barbu, A., Zhu, S.C.: Generalizing Swendsen-Wang to Sampling Arbitrary Posterior Probabilities. IEEE Trans. on Pattern Analysis and Machine Intelligence 27, 1239–1253 (2005)
Malik, J., Belongie, S., Leung, T., Shi, J.: Contour and Texture Analysis for Image Segmentation. International Journal of Computer Vision 43, 7–27 (2001)
Martin, D.R., Fowlkes, C.C., Malik, J.: Learning to Detect Natural Image Boundaries Using Local Brightness, Color, and Texture Cues. IEEE Trans. on Pattern Analysis and Machine Intelligence 26, 530–549 (2004)
Torralba, A.B., Murphy, K.P., Freeman, W.T.: Sharing Visual Features for Multiclass and Multiview Object Detection. IEEE Trans. on Pattern Analysis and Machine Intelligence 29, 854–869 (2007)
Ren, X., Fowlkes, C.C., Malik, J.: Scale-Invariant Contour Completion Using Conditional Random Fields. In: IEEE International Conference on Computer Vision, pp. 1214–1221 (2005)
Berg, A.C., Malik, J.: Geometric blur for template matching. In: IEEE Conf. on Computer Vision and Pattern Recognition, pp. 607–614 (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Besbes, O., Boujemaa, N., Belhadj, Z. (2009). Cue Integration for Urban Area Extraction in Remote Sensing Images. In: Kamel, M., Campilho, A. (eds) Image Analysis and Recognition. ICIAR 2009. Lecture Notes in Computer Science, vol 5627. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02611-9_25
Download citation
DOI: https://doi.org/10.1007/978-3-642-02611-9_25
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-02610-2
Online ISBN: 978-3-642-02611-9
eBook Packages: Computer ScienceComputer Science (R0)