Abstract
Change detection is a main issue in various domains, and especially for remote sensing purposes. Indeed, plethora of geospatial images are available and can be used to update geographical databases. In this paper, we propose a classification-based method to detect changes between a database and a more recent image. It is based both on an efficient training point selection and a hierarchical decision process. This allows to take into account the intrinsic heterogeneity of the objects and themes composing a database while limiting false detection rates. The reliability of the designed framework method is first assessed on simulated data, and then successfully applied on very high resolution satellite images and two land-cover databases.
The original version of this chapter was revised: The copyright line was incorrect. This has been corrected. The Erratum to this chapter is available at DOI: 10.1007/978-3-319-02895-8_64
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Bruzzone, L., Bovolo, F.: A Novel Framework for the Design of Change-Detection Systems for Very-High-Resolution Remote Sensing Images. Proceedings of the IEEE 101(3), 609–630 (2013)
Buttner, G., et al.: Corine Land Cover update 2000. Technical guidelines. European Environment Agency, Copenhagen (2002)
Champion, N., Boldo, D., Pierrot-Deseilligny, M., Stamon, G.: 2D building change detection from high resolution satellite imagery: A two-step hierarchical method based on 3D invariant primitives. PRL 31(10), 1138–1147 (2010)
Demir, B., Minello, L., Bruzzone, L.: An Effective Strategy to Reduce the Labeling Cost in the Definition of Training Sets by Active Learning (2013)
Forman, G.: An extensive empirical study of feature selection metrics for text classification. JMLR 3, 1289–1305 (2003)
Gomez-Chova, L., et al.: A review of kernel methods in remote sensing data analysis. In: Optical Remote Sensing, pp. 171–206. Springer, Heidelberg (2011)
Goyette, N., Jodoin, P.-M., Porikli, F., Konrad, J., Ishwar, P.: A changedetection.net: A new change detection benchmark dataset. In: Proc. IEEE Workshop on Change Detection (CDW 2012) at CVPR 2012, Providence, RI (2012)
Le Bris, A.: Extraction of vineyards out of aerial ortho-image using texture information. In: ISPRS Annals of Photogrammetry, Remote Sensing and the Spatial Information Sciences, Melbourne, Australia (2012)
Marcal, A., Borges, J., Gomes, J., Pinto Da Costa, J.: Land cover update by supervised classification of segmented ASTER images. IJRS 26(7), 1347–1362 (2005)
Miller, O., Pikaz, A., Averbuch, A.: Objects based change detection in a pair of gray-level images. PR 38(11), 1976–1992 (2005)
Nemmour, H., Chibani, Y.: Change detector combination in remotely sensed imagery. In: Advanced Concepts for Intelligent Vision Systems Conference, August 31-September 3, pp. 373–380. ACIVS, Brussels (2004)
Petitjean, F., Inglada, J., Ganarski, P.: Satellite image time series analysis under time warping. IEEE TGRS 50(8), 3081–3095 (2012)
Radke, R.J., Andra, S., Al-Kofahi, O., Roysam, B.: Image change detection algorithms: a systematic survey. IEEE TIP 14(3), 294–307 (2005)
Robin, A., Moisan, L., Hegarat-Mascle, S.: An a-contrario approach for subpixel change detection in satellite imagery. IEEE TPAMI 32(11), 1977–1993 (2010)
Schölkopf, B., Smola, A.J.: Learning with kernels: support vector machines, regularization, optimization and beyond. The MIT Press (2002)
Trias-Sanz, R., Stamon, G., Louchet, J.: Using colour, texture, and hierarchical segmentation for high-resolution remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing 63(2), 156–168 (2008)
Wu, T.F., Lin, C.J., Weng, R.C.: Probability estimates for multi-class classification by pairwise coupling. JMLR 5, 975–1005 (2004)
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Gressin, A., Vincent, N., Mallet, C., Paparoditis, N. (2013). Semantic Approach in Image Change Detection. In: Blanc-Talon, J., Kasinski, A., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2013. Lecture Notes in Computer Science, vol 8192. Springer, Cham. https://doi.org/10.1007/978-3-319-02895-8_40
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DOI: https://doi.org/10.1007/978-3-319-02895-8_40
Publisher Name: Springer, Cham
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