Towards an Intelligent Predictive Model for Analyzing Spatio-Temporal Satellite Image Based on Hidden Markov Chain

Houcine Essid, Imed Riadh Farah, Vincent Barra

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Research Laboratory in Computer Integrated Documentation and Arabized-Documentiel Geniuses and Software, National School of Computer Science Campus, Universitaire de Manouba, Tunis, Tunisie.

DOI: 10.4236/ars.2013.23027                

Abstract

Nowadays remote sensing is an important technique for observing Earth surface applied to different areas such as, land use, urban planning, remote monitoring, real time deformation of the soil that can be associated with earthquakes or landslides, the variations in thickness of the glaciers, the measurement of volume changes in the case of volcanic eruptions, deforestation, etc. To follow the evolution of these phenomena and to predict their future states, many approaches have been proposed. However, these approaches do not respond completely to the specialists who process yet more commonly the data extracted from the images in their studies to predict the future. In this paper, we propose an innovative methodology based on hidden Markov models (HMM). Our approach exploits temporal series of satellite images in order to predict spatio-temporal phenomena. It uses HMM for representing and making prediction concerning any objects in a satellite image. The first step builds a set of feature vectors gathering the available information. The next step uses a Baum-Welch learning algorithm on these vectors for detecting state changes. Finally, the system interprets these changes to make predictions. The performance of our approach is evaluated by tests of space-time interpretation of events conducted over two study sites, using different time series of SPOT images and application to the change in vegetation with LANDSAT images.

Keywords

Satellite Image; Remote Sensing; Hidden Markov Model; Change Detection; Image Processing

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	J. Rodriguez, F. Vos and R. Below, “Annual Disaster Statistical Review 2008: The Numbers and Trends,” Centre for Research on the Epidemiology of Disasters, 2009, pp. 1-33.
[2]	A. Singh, “Digital Change Detection Techniques Using Remotely-Sensed Data,” International Journal of Remote Sensing, Vol. 10, No. 6, 1989, pp. 989-1003. doi:10.1080/01431168908903939
[3]	D. Lu, P. Mausel, E. Brondizio and E. Moran, “Change Detection Techniques,” International Journal of Remote Sensing, Vol. 25, No. 12, 2004, pp. 2365-2407. doi:10.1080/0143116031000139863
[4]	P. Coppin, I. K. Jonckheere, B. Nackaerts and B. Muys, “Digital Change Detection Methods in Ecosystem Monitoring: A Review,” International Journal of Remote Sensing, Vol. 25, No. 9, 2004, pp. 1565-1596. doi:10.1080/0143116031000101675
[5]	M. Lazri, S. Ameur and B. Haddad, “Analyse de Données de Précipitations par Approche Markovienne,” Larhyss Journal, Vol. 6, 2007, pp. 7-20.
[6]	J. F. Mari and F. Le Ber, “Temporal and Spatial Data Mining with Second-Order Hidden Markov Models,” Soft Computing, Vol. 10, No. 5, 2006, pp. 406-414. doi:10.1007/s00500-005-0501-0
[7]	H. Sagan, “Space-Filling Curves,” Springer-Verlag, Berlin, 1994.
[8]	S. Aupetit, M. Slimane and N. Monmarche, “Utilisation des Chaines de Markov Cachées à Substitution de Symboles Pour l’Apprentissage et la Reconnaissance Robuste d’Images,” 2nd Proceeding of MAJECSTIC, 13-15 October 2004, Calais, pp. 245-269.
[9]	L. Aurdal, R.B. Huseby, D. Vikhamar, L. Eikvil and A. Solberg, “Use of Hidden Markov Models and Phenology for Multitemporal Satellite Image Classification: Applications to Mountain Vegetation Classification,” 3rd International Workshop on the Analysis of Multi-Temporal Remote Sensing Images, Biloxi, 16-18 May 2005, pp. 220-224.
[10]	W. Pieczynski, “Triplet Markov Chains and Image Segmentation,” Draft of Chapter 4 in Inverse Problems in Vision and 3D Tomography, Wiley, Hoboken, 2010.
[11]	W. Pieczynski, “Pairwise Markov Chains,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 25, No. 5, 2003, pp. 634-639. doi:10.1109/TPAMI.2003.1195998
[12]	W. Pieczynski, C. Hulard and T. Veit, “Triplet Markov Chains in Hidden Signal Restoration,” Proceedings of SPIE 4885, Image and Signal Processing for Remote Sensing VIII, Crete, 22-27 September 2002, pp. 58-68.
[13]	C. Carincotte, S. Derrode and S. Bourennane, “Unsupervised Change Detection non SAR Images Using Fuzzy Hidden Markov Chains,” IEEE Transactions on Geosciences and Remote Sensing, Vol. 44, No. 2, 2005, pp. 432-441.
[14]	S. Derrode and W. Pieczynski, “Signal and Image Segmentation Using Pairwise Markov Chains,” IEEE Transactions on Signal Processing, Vol. 52, No. 9, 2004, pp. 2477-2489. doi:10.1109/TSP.2004.832015
[15]	A. B. Wijanarto, “Application of Markov Change Detection Technique for Detecting Landsat ETM Derived Land Cover Change over Banten Bay,” Jurnal Ilmiah Geomatika, Vol. 12, No. 1, 2006, pp. 11-21.
[16]	C. Lacoste, X. Descombes, J. Zerubia and N. Baghdadi, “Extraction Automatique des Réseaux Linéiques à Partir d’Images Satellitaires et Aériennes par Processus Markov Objet,” Bulletin de la SFPT, Vol. 170, 2003, pp. 13-22.
[17]	C. Song, “Spectral Mixture Analysis for Subpixel Vegetation Fractions in the Urban Inveronment: How to Incorporate Endmember Variability?” Remote Sensing of Inveronment, Vol. 95, No. 2, 2005, pp. 248-263. doi:10.1016/j.rse.2005.01.002
[18]	V. Michel, “Analyse des Données,” 4th Edition, Economica, Paris, 1997.
[19]	B.-Y. Kang, D.-W. Kim and Q. Li, “Spatial Homogeneity-Based Fuzzy c-Means Algorithm for Image Segmentation,” Proceedings of Second International Conference on Fuzzy Systems and Knowledge Discovery, Changsha, 27-29 August 2005, Part I.
[20]	D. L. Phum, “Spatial Models for Fuzzy Clustering,” Computer Vision and Image Understanding, Vol. 84, No. 2, 2001, pp. 285-297. doi:10.1006/cviu.2001.0951
[21]	T. Kohonen, “Self-Organizing Maps,” Springer-Verlag, Berlin, 1995.
[22]	L. R. Rabiner, “A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition,” Proceeding of the IEEE, Vol. 77, No. 2, 1989, pp. 257-286. doi:10.1109/5.18626
[23]	S. Kapadia, “Discriminative Training of Hidden Markov Models,” Ph.D. Dissertation, Downing College-University of Cambridge, Cambridge, 1998.
[24]	L. Gueguen, “Extraction d’Information et Compression Conjointes des Séries Temporelles d’Images Satellitaires,” Ph.D. Dissertation, l’école Nationale Supérieure des Télécommunications de Paris, Paris, 2007.
[25]	J. G. Lyon, D. Yuan, R. S. Lunetta and C. D. Elvidge, “A Change Detection Experiment Using Vegetation Indices,” Photogrammetric Engineering and Remote Sensing, Vol. 64, 1998, pp. 143-150.
[26]	B. M. Ranga, G. H. Forrest, J. S. Piers and L. M. Alexander, “The Interpretation of Spectral Vegetation Indexes,” IEEE Transactions on Geosiences and Remote Sensing, Vol. 33, No. 2, 1995, pp. 481-486.