IGARSS 2015 : JULY 26 –31, MILAN, ITALY

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IGARSS 2015 : JULY 26 –31, 2015. MILAN, ITALY REMOTE SENSING : UNDERSTANDING THE EARTH FOR A SAFER WORLD FUSION OF MULTISPECTRAL SATELLITE IMAGE BY QUASI MONTE CARLO SAMPLING METHODS Mohamed KHIDER1, Soumya OURABIA1, Youcef SMARA1 1LTIR, Faculté d’Electronique et d’Informatique, Université des Sciences et de la Technologie Houari Boumedienne, B.P. 32 Bab Ezzouar, 16111, Algiers, Algeria. E-mail: mohamed.khider@ieee.org Introduction The present work proposes the study of satellite image fusion methods applied to ALSAT-2A, by introducing the pseudo and quasi-random Monte Carlo sampling PMC and QMC to reduce the computation time and also as a Pan Sharpening interpolation method, using three methods based on QMC sampling. Example : multispectral image, panchromatic at right QMC distribution. Diagram.1 combination of methods Diagram.2 Histogram matching between IHS Bands and QMC Fig.1 Fusion by Diagram 3 : direct estimation and right by simulated annealing. Fig.6 eigenvectors by QMC in red color. Histogram matching operation Fig.7 Fig.2 QMC norms positions using sorting. Fig.8 2 3 1 histogram matching : between band obtained by IHS and QMC positions of the PCA method. 4 6 5 Diagram.3 PCA interpolation by QMC with simulated annealing Fig.3 PCA QMC method, using simulated annealing , insufficient number of levels and Gamma. Fig.4 Fig.9 Conclusion In this paper, we have implemented differents Pansharpening methods based on QMC sampling, from this study, we have found that (1) it’s possible to diminish the computation time of the covariance matrix using QMC positions this reduces the computation time to 99%. (2) Ability to privilege the spatial or spectral quality with the use of QMC positions and the Euclidean distance between original spectra and those of the QMC positions after fusion, this allows us to increase the quality factor Qps, for this purpose the minimum distance is calculated by simulated annealing method. (3) Interpolation based on QMC positions and matching histograms indicate an improvement in fusion results. As perspectives, we propose the optimization of algorithms and the study of a bigger image database. A more important number of levels and Gamma. Pansharpening by method 3, on the right , result obtained with IHS Fig.5 Method Qwb Red Qwb Green Qwb Blue CC Red CC Green CC Blue Moy(Q) Moy(CC) Qps 1 0,6803 0,6704 0,7977 0,9485 0,9499 0,9010 0,7161 0,9331 0,6682 2 0,7851 0,7125 0,5507 0,9482 0,9558 0,9306 0,6827 0,9449 0,6451 3 0,8140 0,8323 0,8583 0,8963 0,8879 0,8204 0,8349 0,8682 0,7249 IHS 0,6986 0,6233 0,4452 0,9825 0,9861 0,9615 0,5891 0,9767 0,5754 BROVEY 0,2254 0,2217 0,2138 0,9818 0,9620 0,2203 0,2152 HCS 0,5730 0,5366 0,4642 0,9801 0,9850 0,9623 0,5246 0,9758 0,5119 HCS Smart 0,5733 0,5368 0,9800 0,9621 0,5248 0,9757 0,5120 PCA 0,6924 0,6555 0,7926 0,9486 0,9494 0,9025 0,7135 0,9335 0,6661 CN 0,7090 0,6169 0,4644 0,5968 0,5829 Gram Schmidt 0,2047 0,1997 0,1877 0,9302 0,9276 0,8908 0,1974 0,9162 0,1808 LMM 0,7758 0,7008 0,5593 0,9469 0,9546 0,9357 0,6786 0,9457 0,6418 LMVM 0,9208 0,9144 0,9089 0,8224 0,8250 0,8054 0,9147 0,8176 0,7479 GLP 0,6147 0,5979 0,5691 0,9911 0,9867 0,9770 0,5939 0,9849 0,5850 DWT 0,7386 0,6524 0,4703 0,9337 0,9311 0,8943 0,6204 0,9197 0,5707 C. Price 0,8522 0,8449 0,8472 0,8895 0,8846 0,8481 0,8754 0,7424 References [1] Hayes, B. : Excursions quasi-aléatoires. Pour la Science. n° 410. p 54-60. décembre 2011. [2] Xiaoqun Wang, 2001 : Variance reduction techniques and quasi-Monte Carlo methods. Journal of Computational and Applied Mathematics, 132, p 309-318. Elsevier. [3] Padwick C., Deskevich M., Pacifici F. and Smallwood S., 2010 : Worldview-2 PAN-Sharpening. ASPRS 2010 Annual Conference, San Diego, California, April 26-30, 2010. [4] Zhou Wang and Alan C. Bovik, 2002 : A Universal Image Quality Index. IEEE Signal Processing Letters. Vol XX, No Y. march 2002. [5] Paskov S.H. et Traub J.E, 1995 : Faster Valuation of Financial Derivatives. Journal of Portfolio Management. Vol 22. p 113-121. [6] Henri Faure, 2007 : Van der Corput sequences towards general (0,1)- sequences in base b.Journal de théorie des nombres de Bordeaux 19. p 125-140. [7] Manuel Bompard, 2011 : Modèles de substitution pour l’optimisation globale de forme en aérodynamique et méthode locale sans paramétrisation. Thèse Doctorat en Sciences. Université de Nice-Sophia Antipolis. [8] Niederreiter, H. 1992 : Random Number Generation and Quasi-Monte-Carlo Methods. CMBS-NSF, Vol.63. Philadelphia : SIAM. Table.1 Comparison of Pansharpening methods by using Qps factor.