The Community for Technology Leaders
RSS Icon
Subscribe
Issue No.11 - November (2010 vol.32)
pp: 1977-1993
Amandine Robin , University of the Witwatersrand, South Africa
Lionel Moisan , Universite Paris-Descartes (MAP5), Paris
Sylvie Le Hégarat-Mascle , Universite Paris Sud Orsay, Orsay
ABSTRACT
This paper presents a new method for unsupervised subpixel change detection using image series. The method is based on the definition of a probabilistic criterion capable of assessing the level of coherence of an image series relative to a reference classification with a finer resolution. In opposition to approaches based on an a priori model of the data, the model developed here is based on the rejection of a nonstructured model—called a-contrario model—by the observation of structured data. This coherence measure is the core of a stochastic algorithm which automatically selects the image subdomain representing the most likely changes. A theoretical analysis of this model is led to predict its performances, in particular regarding the contrast level of the image as well as the number of change pixels in the image. Numerical simulations are also presented that confirm the high robustness of the method and its capacity to detect changes impacting more than 25 percent of a considered pixel under average conditions. An application to land-cover change detection is then provided using time series of satellite images.
INDEX TERMS
Change detection, a-contrario modeling, significance test, subpixel, mixture model, image series.
CITATION
Amandine Robin, Lionel Moisan, Sylvie Le Hégarat-Mascle, "An A-Contrario Approach for Subpixel Change Detection in Satellite Imagery", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.32, no. 11, pp. 1977-1993, November 2010, doi:10.1109/TPAMI.2010.37
REFERENCES
[1] S. Bontemps, P. Bogaert, N. Titeux, and P. Defourny, "An Object-Based Change Detection Method Accounting for Temporal Dependences in Time Series with Medium to Coarse Spatial Resolution," Remote Sensing of Environment, vol. 112, pp. 3181-3191, 2008.
[2] K. Conradsen, A. Nielsen, J. Schou, and H. Skriver, "A Test Statistic in the Complex Wishart Distribution and Its Application to Change Detection in Polarimetric Sar Data," IEEE Trans. Geoscience and Remote Sensing, vol. 41, no. 1, pp. 4-19, Jan. 2003.
[3] L. Bruzzone and D. Prieto, "An Adaptive Semiparametric and Context-Based Approach to Unsupervised Change Detection in Multitemporal Remote-Sensing Images," IEEE Trans. Image Processing, vol. 11, no. 4, pp. 452-466, Apr. 2002.
[4] M. Bosc, F. Heitz, J. Armspach, I. Namer, D. Gounot, and L. Rumbach, "Automatic Change Detection in Multimodal Serial MRI: Application to Multiple Sclerosis Lesion Evolution," Neuroimage, vol. 20, pp. 643-656, 2003.
[5] D. Rey, G. Subsol, H. Delingette, and N. Ayache, "Automatic Detection and Segmentation of Evolving Processes in 3D Medical Images: Application to Multiple Sclerosis," Medical Image Analysis, vol. 6, no. 2, pp. 163-179, June 2002.
[6] A. Adam, E. Rivlin, I. Shimshoni, and D. Reinitz, "Robust Real-Time Unusual Event Detection Using Multiple Fixed-Location Monitors," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 30, no. 3, pp. 555-560, Mar. 2008.
[7] R. Collins, A. Lipton, and T. Kanade, "Introduction to the Special Section on Video Surveillance," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 22, no. 8, pp. 745-746, Aug. 2000.
[8] P. Agouris, S. Gyftakis, and A. Stefanidis, "Uncertainty in Image-Based Change Detection," Proc. Int'l Symp. Spatial Accuracy, p. 18, 2000.
[9] S. Le Hégarat-Mascle and R. Seltz, "Automatic Change Detection by Evidential Fusion of Change Indices," Remote Sensing of Environment, vol. 91, pp. 390-404, 2004.
[10] F. Bovolo and L. Bruzzone, "A Theoretical Framework for Unsupervised Change Detection Based on Change Vector Analysis in the Polar Domain," IEEE Trans. Geoscience and Remote Sensing, vol. 45, no. 1, pp. 218-236, Jan. 2007.
[11] L. Bruzzone and D. Prieto, "Automatic Analysis of the Difference Image for Unsupervised Change Detection," IEEE Trans. Geoscience and Remote Sensing, vol. 38, no. 3, pp. 1171-1182, May 2000.
[12] W. Malila, "Change Vector Analysis: An Approach for Detecting Forest Changes with Landsat," Proc. Ann. Symp. Machine Processing of Remotely Sensed Data, pp. 326-335, 1980.
[13] C. Clifton, "Change Detection in Overhead Imagery Using Neural Networks," Applied Intelligence, vol. 18, pp. 215-234, 2003.
[14] A. Elfishawy, S. Kesler, and A. Abutaleb, "Adaptative Algorithms for Change Detection in Image Sequence," Signal Processing, vol. 23, no. 2, pp. 179-191, 1991.
[15] T. Kasetkasem and P. Varshney, "An Image Change Detection Algorithm Based on Markov Random Field Models," IEEE Trans. Geoscience and Remote Sensing, vol. 40, no. 8, pp. 1815-1823, Aug. 2002.
[16] L. Bruzzone and D. Prieto, "An Adaptative Parcel-Based Technique for Unsupervised Change Detection," Int'l J. Remote Sensing, vol. 21, no. 4, pp. 817-822, 2000.
[17] S. Ghosh, L. Bruzzone, S. Patra, F. Bovolo, and A. Ghosh, "A Context-Sensitive Technique for Unsupervised Change Detection Based on Hopfield-Type Neural Networks," IEEE Trans. Geoscience and Remote Sensing, vol. 45, no. 3, pp. 778-788, Mar. 2007.
[18] R. Radke, S. Andra, O. Al Kohafi, and B. Roysam, "Image Change Detection Algorithms: A Systematic Survey," IEEE Trans. Image Processing, vol. 14, no. 3, pp. 294-307, Mar. 2005.
[19] F. Melgani, G. Moser, and S. Serpico, "Unsupervised Change Detection Methods for Remote Sensing Images," Optical Eng., vol. 41, no. 12, pp. 81-90, 2002.
[20] A. Huertas and G. Medioni, "Detection of Intensity Changes with Subpixel Accuracy Using Laplacian-Gaussian Masks," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 8, no. 5, pp. 651-664, Sept. 1986.
[21] L. Yang, G. Xian, J.M. Klaver, and B. Deal, "Urban Land-Cover Change Detection through Sub-Pixel Imperviousness Mapping Using Remotely Sensed Data," Photogrammetric Eng. and Remote Sensing, vol. 69, no. 9, pp. 1003-1010, 2003.
[22] D. Manolakis, C. Siracusa, and G. Shaw, "Hyperspectral Subpixel Target Detection Using the Linear Mixing Model: Analysis of Hyperspectral Image Data," IEEE Trans. Geoscience and Remote Sensing, vol. 39, no. 7, pp. 1392-1409, July 2001.
[23] S. Le Hégarat-Mascle, C. Ottlé, and C. Guérin, "Land Cover Change Detection at Coarse Spatial Scales Based on Iterative Estimation and Previous State Information," Remote Sensing of Environment, vol. 95, pp. 464-479, 2005.
[24] A. Desolneux, L. Moisan, and J. Morel, "Meaningful Alignments," Int'l J. Computer Vision, vol. 40, no. 1, pp. 7-23, 2000.
[25] A. Desolneux, L. Moisan, and J. Morel, "A Grouping Principle and Four Applications," IEEE Trans. Pattern and Machine Intelligence, vol. 25, no. 4, pp. 508-513, Apr. 2003.
[26] L. Moisan and B. Stival, "A Probabilistic Criterion to Detect Rigid Point Matches between Two Images and Estimate the Fundamental Matrix," Int'l J. Computer Vision, vol. 57, no. 3, pp. 201-218, 2004.
[27] F. Cao, T. Veit, and P. Bouthemy, "Image Comparison and Motion Detection by A Contrario Methods," Computational Vision in Neural and Machine Systems, L. Harris and M. Jenkin, eds., Cambridge Univ. Press, 2005.
[28] B. Grosjean and L. Moisan, "A-Contrario Detectability of Spots in Textured Backgrounds," J. Math. Imaging and Vision, vol. 33, no. 3, pp. 313-337, 2009.
[29] D. Lowe, Perceptual Organization and Visual Recognition. Kluwer Academic Publishers, 1985.
[30] H. Horwitz, R. Nalepka, P. Hyde, and J. Morgenstern, "Estimating the Proportions of Objects within a Single Resolution Element of a Multispectral Scanner," Proc. Seventh Int'l Symp. Remote Sensing of Environment, pp. 1307-1320, 1971.
[31] R. Faivre and A. Fischer, "Predicting Crop Reflectances Using Satellite Data Observing Mixed Pixels," J. Agricultural, Biological, and Environmental Statistics, vol. 2, pp. 87-107, 1997.
[32] H. Cardot, R. Faivre, and M. Goulard, "Functional Approaches for Predicting Land Use with the Temporal Evolution of Coarse Resolution Remote Sensing Data," J. Applied Statistics, vol. 30, pp. 1185-1199, 2003.
[33] M. Fischler and R. Bolles, "Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography," Comm. ACM, vol. 24, pp. 381-385, 1981.
[34] Y. Hochberg and A. Tamhane, Multiple Comparison Procedures. John Wiley & Sons, 1987.
[35] G. Saporta, Probabilités, Analyse des Données et Statistique. TECHNIP, 1990.
[36] C. Bonferroni, "Teoria Statistica delle Classi et Calcolo delle Probabilita," Pubblicazioni del Instituto Superiore de Scienze Economiche e Commerciali di Firenze, vol. 8, pp. 3-62, 1936.
[37] Y. Hochberg, "A Sharper Bonferroni Procedure for Multiple Tests of Significance," Biometrika, vol. 75, pp. 800-803, 1988.
[38] Y. Benjamini and Y. Hochberg, "Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing," J. Royal Statistical Soc., vol. 57, no. 1, pp. 289-300, 1995.
[39] Z. Zhang, R. Deriche, O. Faugeras, and Q.-T. Luong, "A Robust Technique for Matching Two Uncalibrated Images through the Recovery of the Unknown Epipolar Geometry," Artificial Intelligence J., vol. 78, pp. 87-119, 1994.
[40] A. Robin, S. Le Hégarat-Mascle, and L. Moisan, "Unsupervised Subpixelic Classification Using Coarse Resolution Time Series and Structural Information," IEEE Trans. Geoscience and Remote Sensing, vol. 46, no. 5, pp. 1359-1374, May 2008.
[41] G. Moser and S. Serpico, "Generalized Minimum-Error Thresholding for Unsupervised Change Detection from Sar Amplitude Imagery," IEEE Trans. Geoscience and Remote Sensing, vol. 44, no. 10, pp. 2972-2982, Oct. 2006.
[42] L. Bruzzone and D. Prieto, "A Minimum Cost Thresholding Technique for Unsupervised Change Detection," Int'l J. Remote Sensing, vol. 21, no. 18, pp. 3539-3544, 2000.
[43] T. Fung and E. Le Drew, "The Determination of Optimal Threshold Levels for Change Detection Using Various Accuracy Indices," Photogrammetric Eng. and Remote Sensing, vol. 54, no. 10, pp. 1449-1454, 1988.
49 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool