1. 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.
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
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Table.1
Comparison of Pansharpening methods by using Qps factor.