Bayesian scale space analysis of temporal changes in satellite images

References

• L. Bruzzone and D.F. Prieto, Automatic analysis of the difference image for unsupervised change detection, IEEE Trans. Geosci. Remote Sens. 38 (2000), pp. 1171–1182. doi: 10.1109/36.843009
   Web of Science ®Google Scholar
• P. Chaudhuri and J.S. Marron, SiZer for exploration of structures in curves, J. Am. Stat. Assoc. 94 (1999), pp. 807–823. doi: 10.1080/01621459.1999.10474186
   Web of Science ®Google Scholar
• P. Chaudhuri and J.S. Marron, Scale space view of curve estimation, Ann. Stat. 28 (2000), pp. 408–428. doi: 10.1214/aos/1016218224
   Web of Science ®Google Scholar
• P. Coppin, I. Jonckheere, K. Nackaerts, B. Muys, and E. Lambin, Digital change detection methods in ecosystem monitoring: A review, Int. J. Remote Sens. 25 (2004), pp. 1565–1596. doi: 10.1080/0143116031000101675
   Web of Science ®Google Scholar
• P. Erästö and L. Holmström, Bayesian multiscale smoothing for making inferences about features in scatter plots, J. Comput. Graph. Stat. 14 (2005), pp. 569–589. doi: 10.1198/106186005X59315
   Web of Science ®Google Scholar
• A. Ghosh, N.S. Mishra, and S. Ghosh, Fuzzy clustering algorithms for unsupervised change detection in remote sensing images, Inform. Sci. 181 (2011), pp. 699–715. Available at http://www.sciencedirect.com/science/article/pii/S0020025510005153 doi: 10.1016/j.ins.2010.10.016
   Web of Science ®Google Scholar
• F. Godtliebsen, J.S. Marron, and P. Chaudhuri, Statistical significance of features in digital images, Image Vis. Comput. 22 (2004), pp. 1093–1104. doi: 10.1016/j.imavis.2004.05.002
   Web of Science ®Google Scholar
• G.J. Hay, P. Dubé, A. Bouchard, and D.J. Marceau, A scale-space primer for exploring and quantifying complex landscapes, Ecol. Model. 153 (2002), pp. 27–49. doi: 10.1016/S0304-3800(01)00500-2
   Web of Science ®Google Scholar
• L. Holmström, BSiZer, Wiley Interdiscip. Rev.: Comput. Stat. 2 (2010), pp. 526–534. Available at http://dx.doi.org/10.1002/wics.115 doi: 10.1002/wics.115
   Google Scholar
• L. Holmström, Scale space methods, Wiley Interdiscip. Rev.: Comput. Stat. 2 (2010), pp. 150–159. Available at http://dx.doi.org/10.1002/wics.79 doi: 10.1002/wics.79
   Google Scholar
• L. Holmström and L. Pasanen, Bayesian scale space analysis of differences in images, Technometrics 54 (2012), pp. 16–29. doi: 10.1080/00401706.2012.648862
   Web of Science ®Google Scholar
• L. Holmström, L. Pasanen, R. Furrer, and S.R. Sain, Scale space multiresolution analysis of random signals, Comput. Stat. Data Anal. 55 (2011), pp. 2840–2855. Available at http://dx.doi.org/10.1016/j.csda.2011.04.011 doi: 10.1016/j.csda.2011.04.011
   Web of Science ®Google Scholar
• J. Inglada and G. Mercier, A new statistical similarity measure for change detection in multitemporal sar images and its extension to multiscale change analysis, IEEE Trans. Geosci. Remote Sens. 45 (2007), pp. 1432–1445. doi: 10.1109/TGRS.2007.893568
   Web of Science ®Google Scholar
• J.R. Jensen (ed.), Urban/Suburban Land Use Analysis, Manual of Remote Sensing, Vol. 2, chapter 30, 2nd ed., American Society of Photogrammetry, New York, 1983, pp. 1571–1666.
   Google Scholar
• J. Kaipio and E. Somersalo, Statistical and Computational Inverse Problems, Applied Mathematical Sciences, Springer Verlag, Berlin, 2004.
   Google Scholar
• S. Liang, Quantitative Remote Sensing of Land Surfaces, John Wiley & Sons, Inc., Hoboken, NJ, 2004.
   Google Scholar
• D. Lu, P. Mausel, E. Brondízio, and E. Moran, Change detection techniques, Int. J. Remote Sens. 25 (2004), pp. 2365–2407. doi: 10.1080/0143116031000139863
   Web of Science ®Google Scholar
• R.S. Lunetta, J.F. Knight, J. Ediriwickrema, J.G. Lyon, and L.D. Worthy, Land-cover change detection using multi-temporal modis NDVI data, Remote Sens. Environ. 105 (2006), pp. 142–154. Available at http://www.sciencedirect.com/science/article/pii/S00344257060 02549 doi: 10.1016/j.rse.2006.06.018
   Web of Science ®Google Scholar
• F. Melgani, G. Moser, and S.B. Serpico, Unsupervised change-detection methods for remote-sensing images, Opt. Eng. 41 (2002), pp. 3288–3297. doi: 10.1117/1.1518995
   Web of Science ®Google Scholar
• G. Moser, E. Angiati, and S.B. Serpico, Multiscale unsupervised change detection on optical images by Markov random fields and wavelets, IEEE Geosci. Remote Sens. Lett. 8 (2011), pp. 725–729. doi: 10.1109/LGRS.2010.2102333
   Web of Science ®Google Scholar
• L. Pasanen and L. Holmström, Bayesian scale space analysis of images, 2013 8th International Symposium on Image and Signal Processing and Analysis (ISPA), Trieste, Italy, 2013.
   Google Scholar
• H. Rue and L. Held, Gaussian Markov Random Fields: Theory and Applications, Monographs on Statistics and Applied Probability, Vol. 104, Chapman & Hall, London, 2005.
   Google Scholar
• A. Singh, Review article digital change detection techniques using remotely-sensed data, Int. J. Remote Sens. 10 (1989), pp. 989–1003. doi: 10.1080/01431168908903939
   Web of Science ®Google Scholar
• S.P. Teng, Y.K. Chen, K.S. Cheng, and H.C. Lo, Hypothesis-test-based landcover change detection using multi-temporal satellite images – A comparative study, Adv. Space Res. 41 (2008), pp. 1744–1754. tAvailable at http://www.sciencedirect.com/science/article/pii/S0273117707007144 doi: 10.1016/j.asr.2007.06.064
   Web of Science ®Google Scholar
• G. Winkler, Image Analysis, Random Fields and Markov Chain Monte Carlo Methods: A Mathematical Introduction, Stochastic Modelling and Applied Probability, 2nd ed., Springer Verlag, Berlin, 2003.
   Google Scholar
• D. Yuan and C.D. Elvidge, Comparison of relative radiometric normalization techniques, ISPRS J. Photogrammetry Remote Sens. 51 (1996), pp. 117–126. doi: 10.1016/0924-2716(96)00018-4
   Web of Science ®Google Scholar