International Journal of Image, Graphics and Signal Processing (IJIGSP)
IJIGSP Vol. 5, No. 10, 8 Aug. 2013
Cover page and Table of Contents: PDF (size: 1814KB)
A Method for Post-hazard Assessment Through Topography Analysis using Regional Segmentation for Multi-temporal Satellite Imagery: A Case Study of 2011 Tohuku Earthquake Region
Full Text (PDF, 1814KB), PP.63-75
Views: 0 Downloads: 0
Author(s)
Index Terms
The 2011 Tohoku earthquake, ASAR, Graph cut, Regional segmentation, Region damage, Fractal analysis
Abstract
Non-rigid image registration in extracting deformation map for two satellite images of the same region before and after earthquake occurrence based on measure of intensity dissimilarity C(Ir, T(If)) can play a significant role in post hazard analysis. In this paper, we have proposed a novel image transformation and regional segmentation of the same visualized region by assigning displacement label to change in intensity using Advanced Synthetic Aperture Radar (ASAR) satellite images. We used graph cut based non rigid registraion with a data term and a smoothness term for assigning markovianity between neighboring pixels. Displacement labels has been directly assigned from this data term for small intensity difference. Secondly, our data term imposes stricter penalty for intensity mismatches and hence yields higher registration accuracy.
Based on the satellite image analysis through image segmentation, it is found that the area of .997 km2 for the Honshu region was a maximum damage zone localized in the coastal belt of NE Japan fore-arc region. A further objective has been to correlate fractal analysis of seismic clustering behavior with image segmentation suggesting that increase in the fractal dimension coefficient is associated with the deviation of the pixel values that gives a metric of the devastation of the de-clustered region.
Cite This Paper
Pushan Kumar Dutta, O.P. Mishra, M.K.Naskar,"A Method for Post-hazard Assessment Through Topography Analysis using Regional Segmentation for Multi-temporal Satellite Imagery: A Case Study of 2011 Tohuku Earthquake Region", IJIGSP, vol.5, no.10, pp. 63-75, 2013. DOI: 10.5815/ijigsp.2013.10.08
Reference
[1]Mishra, O.P., Zhao D., Umino N. and Hasegawa A.,2003.Tomography of northeast Japan forearc and its implications for interplate seismic coupling, Geophysical Research Letters, 30 (16), 1850,doi:10.1029/2003GL017736.
[2]Mishra, O.P.,2004, Lithospheric heterogeneities and seismotectonics of NE Japan forearc and Indian regions, D.Sc. thesis, GRC, Ehime University, Japan, 2004, 223p.
[3]Nagai, R.,Kikuchi, M. and Yamanaka Y.,2001.Comparative study on the source process of recurrent large earthquakes in Sanriku-oki Region: The 1968 Tokachi-oki earthquake and the 1994 Sanriku-oki earthquake (in Japanese with English abstract), Zisin, 54, 267– 280.
[4]U.S.GeologicalSurvey,2011.Earthquake Hazards Program,http://earthquake. usgs.gov /earthquakes /recenteqsww/ ,viewed 27th Oct, 2011.
[5]Brown, L., 1992. A survey of image registration techniques. ACM Computing Surveys, vol. 24(4), pp. 325–376, doi:10.1145/146370.146374.
[6]Zitova, B. and Flusser, J.,2003. Image registration methods: a survey. Image and Vision Computing, 21, 977–1000, doi:10.1016/S0262-8856(03)00137-9 .
[7]Holia, M. and Thakar, V.K.,2009.Image registration for recovering affine transformation using Nelder Mead Simplex method for optimization,Avalaible at www.cscjournals.org/csc/manuscript/Journals/IJIP/volume3/IJIP-76.pdf.
[8]Radke, R.J., Andra, S., Al-Kofahi, O., Roysam, B.,2005. Image change detection algorithms: a systematic survey.In: Image Processing, IEEE Transactions on Signal Processing 14(3): 294 - 307 ISSN: 1057-7149. doi:10.1109/TIP.2004.838698.
[9]Deng, Y., Manjunath, B. Shin, H.,1999. Color image segmentation, In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, p. 2446.
[10]Meyer, F. and Beucher, S.,1992. The morphological approach of segmentation:the watershed transformation,In: Mathematical Morphology in Image Processing, E. Dougherty, Ed. Marcel Dekker, New York.
[11]Marfil, R., Molina-Tanco, L., Bandera, A., Rodrguez, J.,Sandoval, F.,2006.Pyramid segmentation algorithms revisited, Pattern Recognition,vol. 39, no. 8, pp. 1430 – 1451.
[12]Thirion, J.,1998. Image matching as a diffusion process: an analogy with maxwell's demons. Medical Image Analysis, vol. 2 (3), pp. 243–260.
[13]Xiang, S., Pan, C., Nie, F., Zhang, C.,2010. Turbo-pixel segmentation using eigen-images.In: IEEE Transactions on Image Processing, vol. 19, pp. 3024–3034, November 2010.
[14]Boykov, Y., Veksler, O., and Zabih, R., 2001a. Fast approximate energy minimization via graph cuts. In: IEEE Transactions on Pattern Analysis and Machine Intelligence,23(11):1222–1239, Crossref.
[15]Priese, L. and Rehrmann,V.,1993. On hierarchical color segmentation and applications,In: Proc of the Conference on Computer Vision and Pattern Recognition, pp. 633–634.
[16]Schubert, A., Small, D., Nuno, M, Meier, E. ,2008. ASAR Product Consistency and Geolocation Accuracy. In: Proceedings of the CEOS SAR Cal/Val Workshop, Oberpfaffenhofen, Germany, November 2008.[Crossref]
[17]Yamaguchi, Y., Kahle, A.B.,Tsu, H.,Kawakami, T., Pniel, M.,1998.Overview of Advanced Spaceborne Thermal Emission and Reflectance Radiometer (ASTER).In: IEEE Trans. Geosci. Remote Sens., 36, pp. 1062−1071, doi: 10.1109/36.700991 .
[18]Meng, Li, Chuanjiang, He and Yi, Zhan,2011.Adaptive level-set evolution without initial contours for image segmentation, J. Electron. Imaging 20, 023004 , doi:10.1117/ 1.3574 770.
[19]Tang, T. W., & Chung, A. C. (2007). Non-rigid image registration using graph-cuts. In Medical Image Computing and Computer-Assisted Intervention–MICCAI 2007 (pp. 916-924). Springer Berlin Heidelberg.
[20]Kato, Z., Pong, T.C., Chung-Mong, L.,2001.Color image segmentation and parameter estimation in a Markovian framework, Pattern Recognition Letters 22 (3–4) 309–321.
[21]Miller, M.I., Joshi, S.C. and Christensen, G.E.,1998.Large deformation fluid diffeomorphisms for landmark and image matching. In: Toga, A., ed.: Brain Warping.
[22]Boykov, Y., Lee, V.S., Rusinek, H., Bansal, R.,2001b. Segmentation of dynamic N-D data sets via graph cuts using markov models. In Medical Image Computing and Computer-Assisted Intervention, (MICCAI), pp. 1058-1066. Crossref
[23]Greig, D., Porteous, B., Seheult, A.,1989. Exact maximum a posteriori estimation for binary images. Journal of the Royal Statistical Society. Series B, 51(2):271–279.
[24]Veksler, O. and Boykov, Y.,2004. Fast approximate energy minimization via graph cuts.In: PAMI, vol. 23 (11), pp. 1222–1239.
[25]Sun, W., Xu, G., Gong, P., Liang, S.,2006. Fractal Analysis of Remotely Sensed Images: A review of methods and applications, Review Article, International Journal of Remote Sensing, Vol. 27, No. 22, 4963-4990.
[26]CHEN Yong and CHEN Ling. 1998. The Fractal Geometry [M]. Beijing: Seismological Press, 163~167 (in Chinese).
[27]Lombaert H., Sun Y., Cheriet F. Landmark-Based Non-rigid Registration Via Graph Cuts. In: Proc. of the 4th International Conference, ICIAR 2007, LNCS, Vol. 4633, pp.166~175,2007.
[28]Frieden, B. R.,1972. Restoring with maximum likelihood and maximum entropy, Journal of Optical Society America, 62:511–518.
[29]Lu C. , Mai Y.-W. and Xie H. A sudden drop of fractal dimension: a likely precursor of catastrophic failure in disordered media, Philosophical Magazine Letters,Vol. 85(1),pg 33–40,2005.
[30]Kohli, P., Ladicky, L., Torr, P.H.S.,2009. Robust Higher Order Potentials for Enforcing Label Consistency," International Journal of Computer Vision, vol. 82, 302-324.
[31]Boykov, Ya.,Veksler, O.and Zabih, R., 1998. Markov random fields with efficient approximations. In: IEEE Conference on Computer Vision and Pattern Recognition,Vol:23(11); pages 648-655,doi: 10.1109/CVPR.1998.698673.
[32]Kolmogorov, V. and Zabih, R.,2005. What energy functions can be minimized via graph cuts?" In: IEEE Transactions on PAMI, vol. 26 (2), pp. 147–159.
[33]Boykov, Y. and Jolly, M.P., 2001,Interactive Graph Cuts for Optimal Boundary & Region Segmentation of Objects in N-d Images. In: International Conference on Computer Vision, (ICCV), vol. I, pp. 105-112. [Crossref]
[34]Li, Y., Sun, J. Tang, C. K., Shum, H. Y.,2007.Lazy Snapping. In:Proceedings of ACMSIGGRAPH pp:303-308.
[35]So R.W.K. and. Chung, A.C.S. Learning-based Non-rigid Image Registration using Prior Joint Intensity Distributions with Graph-Cuts, The 18th International Conference on Image Processing, ICIP, (ICIP'11), Brussels, Belgium, September 11 – 14, 2011, pp. 717 – 720.
[36]Rother, C., Blake, A., and Kolmogorov, V. ,2004. Graphcut - Interactive Foreground Extraction Using Iterated Graph Cuts. In:Proceedings of SIGGRAPH,pp.309-314 doi:10.1145/1015706.1015720.
[37]Vezhnevets, V. and Konouchine, V.,2005. GrowCut: Interactive multi- label N-D image segmentation by cellular automata, Proc. of Graphicon, pp.150–156.