ICTACT Journal on Image and Video Processing

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Comparison of Contour Feature Based and Intensity Based Insat-3D Met Images Coregistration for Sub Pixel Accuracies


Affiliations
1 Space Applications Centre, ISRO, Gujarat, India
2 Department of Civil Engineering, SRM University, India
     

   Subscribe/Renew Journal


Image registration in meteorological images that are acquired continuously for their use in weather forecast activities and other related scientific analysis is a critical requirement. Meteorological images are obtained from geostationary orbits in visible, infrared, water vapor channels covering a large frame of several hundreds of kilometres of geographical extent which generally involve bi-directional scanning to cover larger extents. The acquired images have to be guaranteed for their geometric fidelity to a standard of choice among themselves by image registration. Registration of such images require to deal with low contrast, cloud and snow occlusions apart from navigation data uncertainties. Nevertheless, sub pixel accuracies are demanded for image analysis and geophysical parameters derivations. Feature based registration techniques are commonly used and intensity based techniques are also put to use in these contexts rarely. The proposed feature based approach uses a land water boundary data extraction with phase correlation of image blocks and proposed the intensity based approach tackles the same problem without any preprocessing step using a sampler-metric-transform-optimizer procedure. A comparison of these two approaches is pursued here in this article using various channel data sets of INSAT-3D satellite for sub pixel accuracies.

Keywords

Image Registration, Phase Correlation, Mutual Information, Optimization, Deformation, Transforms.
Subscription Login to verify subscription
User
Notifications
Font Size

  • Indian Space Research Organisation, “INSAT-3D”, Available at: http://www.isro.gov.in/Spacecraft/INSAT-3D-ISRO.html, Accessed on 2016.

  • N. Padmanabhan, R. Ramakrishnan and S.B. Gurjar, “Geometric modelling of INSAT-2E CCD Payload and Multi Strip Mosaicking for Geocoding Large Areas”, Current Science, Vol. 86, No. 8, pp. 1113-1121,2004.

  • “GDAL: OGR Projections Tutorial”, Available at: http://www.gdal.org/osr_tutorial.html, Accessed on 2018.

  • L. Kovacs and I.G. Szcnyan, “Development of AVHRR Image Registration in Hungary”, Advanced Space Research, Vol. 17, No. 1, pp. 123-126, 1996.

  • S.N. Katamanov, “Automatic Navigation of One Pixel Accuracy for Meteorological Satellite Imagery”, Proceedings of 1st Russia and Pacific Conference on Computer Technology and Applications, pp. 269-274, 2010.

  • F.S. Patt and R.H. Woodward, “An Automated Method for Navigation Assessment for Earth Survey Sensors using Island Targets”, International Journal of Remote Sensing, Vol. 18, No. 16, pp. 3311-3336,1997.

  • P. Brunel and A. Marsouin, “Operational AVHRR Navigation Results”, International Journal of Remote Sensing, Vol. 21, No. 5, pp. 951-972, 2000.

  • Z. Mao, D. Pan, H. Huang and W. Huang, “Automatic Registration of SeaWiFS and AVHRR Imagery”, International Journal of Remote Sensing, Vol. 22, No. 9, pp. 1725-1735, 2001.

  • L. Cheng, L. Tong, Y. Liu, M. Li and J. Wang, “Automatic Registration of Coastal Remotely Sensed Imagery by Affine Invariant Feature Matching with Shoreline Constraint”, Marine Geodesy, Vol. 37, No. 1, pp. 32-46, 2014.

  • L. Cheng, Y. Pian, Z. Chen, P. Jiang, Y. Liu, G. Chen, P. Du and M. Li, “Hierarchical Filtering Strategy for Registration of Remote Sensing Images of Coral Reefs”, IEEE Journal of Selected Topics in Applied Earth Observation and Remote Sensing, Vol. 9, No. 7, pp. 3304-3314, 2016.

  • M.A. Fischler and R.C. Bolles, “Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography”, Communications of the ACM, Vol. 24, No. 6, pp. 381-395, 1981.

  • R.O. Duda, P.E. Hart and D.G. Stork, “Pattern Classification”, 2nd Edition, Wiley, 2000.

  • S. Eken and A. Sayar, “An Automated Technique to Determine Spatio-Temporal Changes in Satellite Island Images with Vectorization and Spatial Queries”, Sadhana, Vol. 40, No. 1, pp. 121-137, 2015.

  • L. Sun, X. Mi, J. Wei, J. Wang, X. Tian, H. Yu and P. Gan, “A Cloud Detection Algorithm-Generating Method for Remote Sensing Data at Visible to Short-Wave Infrared Wavelengths”, ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 124, pp. 70-88, 2017.

  • H. Stone, M. Orchard and E.C. Chang, “Subpixel Registration of Images”, Proceedings of 33rd Asilomar Conference on Signals, Systems, and Computers, pp. 1446-1452, 1999.

  • W.S. Hoge, “A Subspace Identification Extension to the Phase Correlation Method”, IEEE Transactions on Medical Imaging, Vol. 22 No. 2, pp. 277-280, 2003.

  • F. Homblot, B. Collin and A. Mohammad-Djafari, “Evaluation and Practical Issues of Image Registration using Phase Correlation Methods,” Proceedings of International Conference on Physics in Signal and Image Processing, pp. 113-118, 2005.

  • J. Ren, T. Vlachos and J. Jiang, “Subspace Extension to Phase Correlation Approach for Fast Image Registration”, Proceedings of International Conference on Image Processing, pp. 481-484. 2007.

  • M. Guizar-Sicairos, S.T. Thurman and J.R. Fienup, “Efficient Subpixel Image Registration Algorithms”, Optics Letters, Vol. 33, No. 2, pp. 156-158, 2008.

  • S. Cao, J. Jiang, G. Zhang and Y. Yuan, “An Edge-based Scale-and Affine-Invariant Algorithm for Remote Sensing Image Registration”, International Journal of Remote Sensing, Vol. 34, No. 7, pp. 2301-2326, 2013.

  • F. Maes, A. Collignon, D. Vandermeulen, G. Marchal and P. Suetens, “Multimodality Image Registration by Maximization of Mutual Information”, IEEE Transactions on Medical Imaging, Vol. 16, No. 2, pp. 187-198, 1997.

  • P. Thevenaz and M. Unser, “Optimization of Mutual Information for Multiresolution Image Registration”, IEEE Transactions on Image Processing, Vol. 9, No. 12, pp. 2083-2099, 2000.

  • A.A. Cole-Rhodes, K.L. Johnson, J. Le Moigne and I. Zavorin, “Multiresolution Registration of Remote Sensing imagery by Optimization of Mutual Information using a Stochastic Gradient”, IEEE Transactions on Image Processing, Vol. 12, No. 12, pp. 1495-1511, 2003.

  • S. Klein, J.P.W. Pluim, M. Staring and M.A. Viergever, “Adaptive Stochastic Gradient Descent Optimisation for Image Registration”, International Journal of Computer Vision, Vol. 81, No. 3, pp. 227-239, 2009.

  • S. Klein, M. Staring, K. Murphy, M.A. Viergever and J.P.W. Pluim, “Elastix: A Toolbox for Intensity-Based Medical Image Registration”, IEEE Transactions on Medical Imaging, Vol. 29, No. 1, pp. 196-205,2010.

  • C.M. Kishtawal, S.K. Deb, P.K. Pal and P.C. Joshi, “Estimation of Atmospheric Motion Vectors from Kalpana-1 Imagers”, Journal of Applied Meteorology and Climatology, Vol. 48, No. 11, pp. 2410-2421, 2009.

  • S.K. Deb, C.M. Kishtawal, P.K. Pal and P.C. Joshi, “A Modified Tracers Selection and Tracking Procedure to Derive Winds using Water Vapor Imagers”, Journal of Applied Meteorology and Climate, Vol. 47, No. 12, pp. 3252-3263, 2008.


Abstract Views: 259

PDF Views: 5




  • Comparison of Contour Feature Based and Intensity Based Insat-3D Met Images Coregistration for Sub Pixel Accuracies

Abstract Views: 259  |  PDF Views: 5

Authors

Subbiah Manthira Moorthi
Space Applications Centre, ISRO, Gujarat, India
Ramamoorthy Sivakumar
Department of Civil Engineering, SRM University, India

Abstract


Image registration in meteorological images that are acquired continuously for their use in weather forecast activities and other related scientific analysis is a critical requirement. Meteorological images are obtained from geostationary orbits in visible, infrared, water vapor channels covering a large frame of several hundreds of kilometres of geographical extent which generally involve bi-directional scanning to cover larger extents. The acquired images have to be guaranteed for their geometric fidelity to a standard of choice among themselves by image registration. Registration of such images require to deal with low contrast, cloud and snow occlusions apart from navigation data uncertainties. Nevertheless, sub pixel accuracies are demanded for image analysis and geophysical parameters derivations. Feature based registration techniques are commonly used and intensity based techniques are also put to use in these contexts rarely. The proposed feature based approach uses a land water boundary data extraction with phase correlation of image blocks and proposed the intensity based approach tackles the same problem without any preprocessing step using a sampler-metric-transform-optimizer procedure. A comparison of these two approaches is pursued here in this article using various channel data sets of INSAT-3D satellite for sub pixel accuracies.

Keywords


Image Registration, Phase Correlation, Mutual Information, Optimization, Deformation, Transforms.

References