The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part Bl. Beijing 2008 
(c) original Aqua image 
Figure 4. Mean column power spectrums of the original and destriped MODIS images. 
Figure 3 shows the mean cross-track profiles of the original 
images and the destriped results using the proposed algorithm. 
It can be seen that the rapid fluctuations in the original data are 
strongly reduced in the destriped images. The mean column 
power spectrums of the original and destriped images are 
shown in 
Figure 4. For better visualization of noise reduction, very high 
spectral magnitudes are not plotted. It is easily recognized that 
the value of the power spectrum of the frequency components 
where the pulses exist has been strongly reduced in the 
destriped images. 
The inverse coefficient of variation (ICV) and ratio of noise 
reduction (NR) are employed to take the quantitative analysis. 
The ICV index (Nichol and Vohora, 2004; Rakwatin et al., 
2007; Smith and Curran, 2000) is defined as 
ICV = -^_ (17) 
K d 
where R a is the signal response of a homogeneous image region 
and is calculated by averaging the pixels within a window of a 
given size; R sd refers to the noise components estimated by 
calculating the standard deviation of the pixel. In our 
experiments, we selected two 10x10 homogeneous regions for 
the ICV evaluation. The NR index (Chen et al., 2003; 
Rakwatin et al., 2007) is used to evaluate the image in the 
frequency domain. It is defined by 
NR=— (18) 
where N 0 is the power of the frequency components produced 
by stripes in the original image, and V, stands for that in the 
destriped image. N 0 and V, can be calculated by 
N, = ^P,(D) (19) 
P 
where PfD) is the averaged power spectrum down the columns 
of an image with D being the distance from the origin in 
Fourier space, and p is the stripe noise region of the spectrum. 
The ICV and NR evaluation results are, respectively, shown in 
Table 1 and 
Table 2. The proposed algorithm always obtains the best results 
in the several destriping methods (Butterworth filtering, 
moment matching and histogram matching). 
Original 
Butterworth 
Moment 
Histogram 
Proposed 
Terra 
Sample 1 
24.08 
27.26 
39.32 
43.93 
46.79 
Band 28 
Sample2 
17.27 
23.93 
21.82 
21.49 
25.87 
Aqua 
Sample 1 
7.94 
14.49 
24.42 
22.72 
26.83 
Band 30 
Sample2 
9.66 
15.74 
21.03 
24.86 
30.31 
Table 1. ICVs of the original and destriped MODIS data 
Original 
Butterworth 
Moment 
Histogram 
Proposed 
Terra Band 28 
1.00 
4.39 
15.96 
17.71 
25.81 
Aqua Band 30 
1.00 
4.26 
4.82 
5.05 
7.56 
Table 2. NRs of the original and destriped MODIS data 
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