= à
RMSE, = x= I” (21)
NaN.
Oz, x
Et b*b
CC, m ET 5 (22)
Xp Xp
O x, x, 1x, ly
VO = = ER A. Q3)
ei, ox, mg, * my)
ERGAS -100-Z- (24)
1 N UN
SA z —————
Ny M Nx Gs
Here, €, and x, represent the bth bands of the fused image
and original image, Oi x is the covariance between X, and
x,, mz and, their means, and c, and c, their standard
deviations. The ideal values of the RMSE, CC, UIQI, ERGAS
and SA are, respectively, 0, 1, 1, 0 and 0. The evaluation results
are shown in Table 1. It is seen that the integrated fusion
method obtains the best evaluation values in terms of all the
indices. This verifies the proposed method has the performance
to integrate the complementary
temporal-spatial-spectral images.
information in multiple
Table 1. Evaluation of the fusion results
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B7, 2012
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia
MT fusion MS/HS PAN/HS Integrated
fusion fusion fusion
RMSE 20.040 20.460 16.012 8.818
CC 0.952 0.945 0.969 0.990
UIQI 0.950 0.943 0.967 0.990
ERGAS 6.365 6.570 4.916 2.662
SA 5.659 8.749 8.230 5.509
5. CONCLUSIONS
This paper presents a fusion method for multiple temporal-
spatial-spectral images based on the maximum a posteriori
framework. Simulated experiments validated that the proposed
method has good performance in terms of both visual inspection
and quantitative evaluation. Future works would be carried out
to test the proposed method using real remote sensing images.
ACKNOWLEDGEMENTS
This work was supported by the Major State Basic Research
Development Program (973 Program) under Grant
2011CB707103, the National Natural Science Foundation under
Grant 40971220, 41071269, the Hubei Natural Science
Foundation under Grant 201 1CDA096, and the Foundation of
State Key Laboratory of Remote Sensing Science under Grant
OFSLRSS201114
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