Publications about Change detection

Result of the query in the list of publications :

• 4 Articles
• 1 PhD Thesis and Habilitation
• 7 Conference articles
• 3 Technical and Research Reports

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4 Articles

1 - Building Development Monitoring in Multitemporal Remotely Sensed Image Pairs with Stochastic Birth-Death Dynamics. C. Benedek and X. Descombes and J. Zerubia. IEEE Trans. Pattern Analysis and Machine Intelligence, 34(1): pages 33-50, January 2012. Keywords : Building extraction, Change detection, Marked point process, multiple birth-and-death dynamics. Copyright : IEEE @ARTICLE{benedekPAMI11, author = {Benedek, C. and Descombes, X. and Zerubia, J.}, title = {Building Development Monitoring in Multitemporal Remotely Sensed Image Pairs with Stochastic Birth-Death Dynamics}, year = {2012}, month = {January}, journal = {IEEE Trans. Pattern Analysis and Machine Intelligence}, volume = {34}, number = {1}, pages = {33-50}, url = {http://dx.doi.org/10.1109/TPAMI.2011.94}, keyword = {Building extraction, Change detection, Marked point process, multiple birth-and-death dynamics} } Abstract : In this paper we introduce a new probabilistic method which integrates building extraction with change detection in remotely sensed image pairs. A global optimization process attempts to find the optimal configuration of buildings, considering the observed data, prior knowledge, and interactions between the neighboring building parts. We present methodological contributions in three key issues: (1) We implement a novel object-change modeling approach based on Multitemporal Marked Point Processes, which simultaneously exploits low level change information between the time layers and object level building description to recognize and separate changed and unaltered buildings. (2) To answering the challenges of data heterogeneity in aerial and satellite image repositories, we construct a flexible hierarchical framework which can create various building appearance models from different elementary feature based modules. (3) To simultaneously ensure the convergence, optimality and computation complexity constraints raised by the increased data quantity, we adopt the quick Multiple Birth and Death optimization technique for change detection purposes, and propose a novel non-uniform stochastic object birth process, which generates relevant objects with higher probability based on low-level image features.

2 - On the Illumination Invariance of the Level Lines under Directed Light: Application to Change Detection. P. Weiss and A. Fournier and L. Blanc-Féraud and G. Aubert. SIAM Journal on Imaging Sciences, 4(1): pages 448-471, March 2011. Keywords : Level Lines, topographic map, illumination invariance, Change detection, contrast equalization, remote sensing. @ARTICLE{SIIMS_2011, author = {Weiss, P. and Fournier, A. and Blanc-Féraud, L. and Aubert, G.}, title = {On the Illumination Invariance of the Level Lines under Directed Light: Application to Change Detection}, year = {2011}, month = {March}, journal = {SIAM Journal on Imaging Sciences}, volume = {4}, number = {1}, pages = {448-471}, url = {http://www.math.univ-toulouse.fr/~weiss/Publis/SIIMS_2011_Weiss.pdf}, pdf = {http://www.math.univ-toulouse.fr/~weiss/Publis/SIIMS_2011_Weiss.pdf}, keyword = {Level Lines, topographic map, illumination invariance, Change detection, contrast equalization, remote sensing} } Abstract : We analyze the illumination invariance of the level lines of an image. We show that if the scene surface has Lambertian reflectance and the light is directed, then a necessary and sufficient condition for the level lines to be illumination invariant is that the three-dimensional scene be developable and that its albedo satisfy some geometrical constraints. We then show that the level lines are “almost” invariant for piecewise developable surfaces. Such surfaces fit most of the urban structures. This allows us to devise a fast and simple algorithm that detects changes between pairs of remotely sensed images of urban areas, independently of the lighting conditions. We show the effectiveness of the algorithm both on synthetic OpenGL scenes and real QuickBird images. The synthetic results illustrate the theory developed in this paper. The two real QuickBird images show that the proposed change detection algorithm is discriminant. For easy scenes it achieves a rate of 85% detected changes for 10% false positives, while it reaches a rate of 75% detected changes for 25% false positives on demanding scenes.

3 - Detection of Object Motion Regions in Aerial Image Pairs with a Multi-Layer Markovian Model. C. Benedek and T. Szirányi and Z. Kato and J. Zerubia. IEEE Trans. Image Processing, 18(10): pages 2303-2315, October 2009. Keywords : Change detection, Aerial images, Camera motion, MRF. @ARTICLE{benedekTIP09, author = {Benedek, C. and Szirányi, T. and Kato, Z. and Zerubia, J.}, title = {Detection of Object Motion Regions in Aerial Image Pairs with a Multi-Layer Markovian Model}, year = {2009}, month = {October}, journal = {IEEE Trans. Image Processing}, volume = {18}, number = {10}, pages = {2303-2315}, url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=5089480}, keyword = {Change detection, Aerial images, Camera motion, MRF} } Abstract : We propose a new Bayesian method for detecting the regions of object displacements in aerial image pairs. We use a robust but coarse 2-D image registration algorithm. Our main challenge is to eliminate the registration errors from the extracted change map. We introduce a three-layer Markov Random Field model which integrates information from two different features, and ensures connected homogeneous regions in the segmented images. Validation is given on real aerial photos.

4 - Change Detection in Optical Aerial Images by a Multi-Layer Conditional Mixed Markov Model. C. Benedek and T. Szirányi. IEEE Trans. Geoscience and Remote Sensing, 47(10): pages 3416-3430, October 2009. Keywords : mixed Markov models, Change detection, Aerial images, MAP estimation. Copyright : IEEE @ARTICLE{benedekTGRS09, author = {Benedek, C. and Szirányi, T.}, title = {Change Detection in Optical Aerial Images by a Multi-Layer Conditional Mixed Markov Model}, year = {2009}, month = {October}, journal = {IEEE Trans. Geoscience and Remote Sensing}, volume = {47}, number = {10}, pages = {3416-3430}, url = {http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?isnumber=5257398&arnumber=5169964&count=26&index=11}, keyword = {mixed Markov models, Change detection, Aerial images, MAP estimation} } Abstract : In this paper we propose a probabilistic model for detecting relevant changes in registered aerial image pairs taken with the time differences of several years and in different seasonal conditions. The introduced approach, called the Conditional Mixed Markov model (CXM), is a combination of a mixed Markov model and a conditionally independent random field of signals. The model integrates global intensity statistics with local correlation and contrast features. A global energy optimization process ensures simultaneously optimal local feature selection and smooth, observation-consistent segmentation. Validation is given on real aerial image sets provided by the Hungarian Institute of Geodesy, Cartography and Remote Sensing and Google Earth.

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PhD Thesis and Habilitation

1 - Algorithmes rapides d'optimisation convexe. Application à la reconstruction d'images et à la détection de changements. P. Weiss. PhD Thesis, Universite de Nice Sophia Antipolis, November 2008. Keywords : Convex optimization, nesterov scheme, Sparse representations, Total variation, Change detection, level lines. Copyright : @PHDTHESIS{These_Pweiss, author = {Weiss, P.}, title = {Algorithmes rapides d'optimisation convexe. Application à la reconstruction d'images et à la détection de changements}, year = {2008}, month = {November}, school = {Universite de Nice Sophia Antipolis}, pdf = {http://www.math.univ-toulouse.fr/~weiss/Publis/These_PWEISS_Compressee.pdf}, keyword = {Convex optimization, nesterov scheme, Sparse representations, Total variation, Change detection, level lines} } Résumé : Cette thèse contient des contributions en analyse numérique et en vision par ordinateur. Dans une première partie, nous nous intéressons à la résolution rapide, par des méthodes de premier ordre, de problèmes d'optimisation convexe. Ces problèmes apparaissent naturellement dans de nombreuses tâches telles que la reconstruction d'images, l'échantillonnage compressif ou la décomposition d'images en texture et en géométrie. Ils ont la particularité d'être non différentiables ou très mal conditionnés. On montre qu'en utilisant des propriétés fines des fonctions à minimiser on peut obtenir des algorithmes de minimisation extrêmement efficaces. On analyse systématiquement leurs taux de convergence en utilisant des résultats récents dûs à Y. Nesterov. Les méthodes proposées correspondent - à notre connaissance - à l'état de l'art des méthodes de premier ordre. Dans une deuxième partie, nous nous intéressons au problème de la détection de changements entre deux images satellitaires prises au même endroit à des instants différents. Une des difficultés principales à surmonter pour résoudre ce problème est de s'affranchir des conditions d'illuminations différentes entre les deux prises de vue. Ceci nous mène à l'étude de l'invariance aux changements d'illuminations des lignes de niveau d'une image. On caractérise complètement les scènes qui fournissent des lignes de niveau invariantes. Celles-ci correspondent assez bien à des milieux urbains. On propose alors un algorithme simple de détection de changements qui fournit des résultats très satisfaisants sur des images synthétiques et des images Quickbird réelles.

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7 Conference articles

1 - Building Extraction and Change Detection in Multitemporal Remotely Sensed Images with Multiple Birth and Death Dynamics. C. Benedek and X. Descombes and J. Zerubia. In IEEE Workshop on Applications of Computer Vision (WACV), pages 100-105, Snowbird, Utah, USA, December 2009. Keywords : Marked point process, Change detection, Aerial images, Building extraction, Satellite images. @INPROCEEDINGS{benedekWacv09, author = {Benedek, C. and Descombes, X. and Zerubia, J.}, title = {Building Extraction and Change Detection in Multitemporal Remotely Sensed Images with Multiple Birth and Death Dynamics}, year = {2009}, month = {December}, booktitle = {IEEE Workshop on Applications of Computer Vision (WACV)}, pages = {100-105}, address = {Snowbird, Utah, USA}, pdf = {http://hal.archives-ouvertes.fr/docs/00/42/66/18/PDF/benedekWACV09.pdf}, keyword = {Marked point process, Change detection, Aerial images, Building extraction, Satellite images} } Abstract : In this paper we introduce a new probabilistic method which integrates building extraction with change detection in remotely sensed image pairs. A global optimization process attempts to find the optimal configuration of buildings, considering the observed data, prior knowledge, and interactions between the neighboring building parts. The accuracy is ensured by a Bayesian object model verification, meanwhile the computational cost is significantly decreased by a non-uniform stochastic object birth process, which proposes relevant objects with higher probability based on low-level image features.

2 - Conditional mixed-state model for structural change analysis from very high resolution optical images. B. Belmudez and V. Prinet and J.F. Yao and P. Bouthemy and X. Descombes. In Proc. IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Cape Town, South Africa, July 2009. Keywords : Change detection, mixed Markov models. @INPROCEEDINGS{bel09, author = {Belmudez, B. and Prinet, V. and Yao, J.F. and Bouthemy, P. and Descombes, X.}, title = {Conditional mixed-state model for structural change analysis from very high resolution optical images}, year = {2009}, month = {July}, booktitle = {IGARSS}, address = {Cape Town, South Africa}, url = {http://hal.archives-ouvertes.fr/inria-00398062/}, keyword = {Change detection, mixed Markov models} } Abstract : The present work concerns the analysis of dynamic scenes from earth observation images. We are interested in building a map which, on one hand locates places of change, on the other hand, reconstructs a unique visual information of the non-change areas. We show in this paper that such a problem can naturally be takled with conditional mixed-state random field modeling (mixed-state CRF), where the ”mixed state” refers to the symbolic or continous nature of the unknown variable. The maximum a posteriori (MAP) estimation of the CRF is, through the Hammersley-Clifford theorem, turned into an energy minimisation problem. We tested the model on several Quickbird images and illustrate the quality of the results.

3 - A Mixed Markov Model for Change Detection in Aerial Photos with Large Time Differences. C. Benedek and T. Szirányi. In Proc. International Conference on Pattern Recognition (ICPR), Tampa, USA, December 2008. Keywords : Aerial images, Change detection, mixed Markov models. @INPROCEEDINGS{benedekICPR08, author = {Benedek, C. and Szirányi, T.}, title = {A Mixed Markov Model for Change Detection in Aerial Photos with Large Time Differences}, year = {2008}, month = {December}, booktitle = {Proc. International Conference on Pattern Recognition (ICPR)}, address = {Tampa, USA}, pdf = {http://hal.inria.fr/docs/00/35/91/16/PDF/benedekICPR08.pdf}, keyword = {Aerial images, Change detection, mixed Markov models} } Abstract : In the paper we propose a novel multi-layer Mixed Markov model for detecting relevant changes in registered aerial images taken with significant time differences. The introduced approach combines global intensity statistics with local correlation and contrast features. A global energy optimization process simultaneously ensures optimal local feature selection and smooth, observation-consistent classification. Validation is given on real aerial photos.

4 - A contrast equalization procedure for change detection algorithms: applications to remotely sensed images of urban areas. A. Fournier and P. Weiss and L. Blanc-Féraud and G. Aubert. In International Conference on Pattern Recognition (ICPR), Tampa, USA, December 2008. Keywords : Change detection, Level Lines, remote sensing. Copyright : ©2008 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. @INPROCEEDINGS{l_lines_icpr08, author = {Fournier, A. and Weiss, P. and Blanc-Féraud, L. and Aubert, G.}, title = {A contrast equalization procedure for change detection algorithms: applications to remotely sensed images of urban areas}, year = {2008}, month = {December}, booktitle = {International Conference on Pattern Recognition (ICPR)}, address = {Tampa, USA}, url = {http://www.math.univ-toulouse.fr/~weiss/Publis/Conferences/icpr2008.pdf}, pdf = {http://www.math.univ-toulouse.fr/~weiss/Publis/Conferences/icpr2008.pdf}, keyword = {Change detection, Level Lines, remote sensing} }

5 - Mixing Geometric and Radiometric Features for Change Classification. A. Fournier and X. Descombes and J. Zerubia. In Proc. SPIE Symposium on Electronic Imaging, San Jose, USA, January 2008. Keywords : Change detection, directional Statistics, polygonal approximation, Classification. Copyright : Copyright 2008 SPIE and IS&T. This paper was published in the proceedings of IS&T/SPIE 20th Annual Symposium on Electronic Imaging and is made available as an electronic reprint (preprint) with permission of SPIE and IS&T. One print or electronic copy may be made for personal use only. Systematic or multiple reproduction, distribution to multiple locations via electronic or other means, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited. @INPROCEEDINGS{fournier_spie08, author = {Fournier, A. and Descombes, X. and Zerubia, J.}, title = {Mixing Geometric and Radiometric Features for Change Classification}, year = {2008}, month = {January}, booktitle = {Proc. SPIE Symposium on Electronic Imaging}, address = {San Jose, USA}, url = {http://hal.inria.fr/inria-00269853/fr/}, keyword = {Change detection, directional Statistics, polygonal approximation, Classification} } Abstract : Most basic change detection algorithms use a pixel-based approach. Whereas such approach is quite well defined for monitoring important area changes (such as urban growth monitoring) in low resolution images, an object based approach seems more relevant when the change detection is specifically aimed toward targets (such as small buildings and vehicles). In this paper, we present an approach that mixes radiometric and geometric features to qualify the changed zones. The goal is to establish bounds (appearance, disappearance, substitution ...) between the detected changes and the underlying objects. We proceed by first clustering the change map (containing each pixel bitemporal radiosity) in different classes using the entropy-kmeans algorithm. Assuming that most man-made objects have a polygonal shape, a polygonal approximation algorithm is then used in order to characterize the resulting zone shapes. Hence allowing us to refine the primary rough classification, by integrating the polygon orientations in the state space. Tests are currently conducted on Quickbird data.

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