 Open Access
 Total Downloads : 565
 Authors : Eng. Mohamed Ahmed Ali, Dr. Fawzy Eltohamy H. Amer
 Paper ID : IJERTV3IS080732
 Volume & Issue : Volume 03, Issue 08 (August 2014)
 Published (First Online): 26082014
 ISSN (Online) : 22780181
 Publisher Name : IJERT
 License: This work is licensed under a Creative Commons Attribution 4.0 International License
Evaluation of Atmospheric (MTF) Effects on Satellite Remote Sensing Image Quality
Eng. Mohamed Ahmed Ali
Aircraft Electric Equipment & Armament Military Technical College
Cairo, Egypt
Dr. Fawzy Eltohamy H. Amer Aircraft Electric Equipment & Armament Military Technical College
Cairo, Egypt
Abstractthe nature of remote sensing requires that solar radiation pass through the atmosphere before it is collected by the sensor. Because of this, remotely sensed image, beside the information about the earths surface, it is contaminated by atmospheric effects. In general, the quality of images is degraded due to the atmospheric effects. This image degradation can be quantified by the overall atmospheric MTF, which can divide into, the aerosol MTF and the atmospheric turbulence MTF. In this paper a simulation model is created in order to study the effect of the atmosphere on the image quality. [1]
Keywordsatmospheric MTF, aerosol MTF, turbulence MTF, remote sensing image quality.

INTRODUCTION
Electromagnetic fields propagating through the atmosphere are attenuated by absorption and large angle scattering by aerosols. [2]
Absorption can be viewed as a reduction in the amount of radiation that reaches a sensor while scattering and turbulence result in image blurring and loss of detail. The blurring is quantified to describe its overall degradation effect on sensor performance. This degradation is characterized in terms of an atmospheric MTF. Atmospheric MTF can roughly be described as a reduction in contrast as a function of spatial frequency. The atmospheric MTF can be divided into: an aerosol MTF and turbulence MTF [3].
The imaging systems can usually be considered linear shift invariant (LSI) systems, where the LSI is defined as having the properties
L{af(xc)+bg(xd) =aL{f(xc)}+bL{g(xd)}
Where, a and b are multiplicative constants and c and d are shifting constants. f(x) and g(x) are functions of the independent variable x, and L denotes the LSI operator.
Each system contributor or component has its own impulse response and transfer function including the atmosphere, optics, detector, electronics, mechanical aspects, display, and human vision. The system impulse response can be determined by the convolution of all the component impulse response and the system transfer function can be determined by the multiplication of all the component transfer function (just like circuit analysis). In imaging systems the transfer function is described by the modulation transfer function (MTF) [3].

TURBULENCE MTF
Turbulence results from random fluctuations in the atmospheric refractive index, which causes the light to arrive at different angles at the receiver (sensor). This results in image dancing, distortion, and blurring. The turbulence MTF for long exposures is represented by [4]
Where a is unity for a plane wave and 3/8 for a spherical wave, is the measured radiation wavelength, is the turbulence strength factor, is angular spatial frequency and R is the distance between the object being imaged and the sensor.
For short exposures (about 1 ms or less) the turbulence MTF is
Where D is the aperture diameter of the imaging system and equals 0.5 in the far field and 1 in the near field.
Turbulence MTF can noticeably affect the higher spatial frequencies of thermal images, this effect are attributed either to atmospheric turbulence, which causes deflection of the radiation from its original path.
The index structure parameter ranges from 1×1015m2/3 for weak turbulence to5x1013m2/3/ for strong turbulence. Factors that increase the index structure parameter are strong solar heating, very dry grounds, clear nights with little wind, low altitude, and surface roughness. Factors that provide reductions in the index structure parameter are heavy overcast, wet surfaces, high winds, and high altitude [3].

AEROSOL MTF
In addition to turbulence, there are scattering and absorption caused by aerosols and molecules that exist in the atmosphere. Very little of the scattered light that is dispersed by aerosols reaches the imaging system mostly because of its limited field of view (FOV). Furthermore, some of the scattered light that reaches the receiver may not be detected because of the limited dynamic range of the detector and its limited bandwidth. Part of the unscattered light can be absorbed by such particulates. The scattering and absorption of energy by
the aerosols affects all spatial frequencies, therefore causing edges in the image to be blurred and the image to be smoothed.
The aerosol MTF approximated by a Gaussian form for Simplification is represented by
Where Aa and Sa are the atmospheric effective absorption and scattering coefficients, respectively, and c is the angular spatial cutoff frequency at the aerosol MTF high frequency asymptote. In clear weather, c is determined primarily by the optical instrumentation characteristics such as FOV, dynamic range and spatial frequency bandwidth of the imaging system. [4] The cutoff spatial frequency is approximately (a/) where a is the particulate radius. [3] Typical, examples of some atmospheric particles are shown in Table (I) and scattering coefficients in different conditions in table (II). [3]

PROPOSED ALGORITHM
The algorithm was done using MATLAB Simulink tools as in Fig. 1,
Fig (1) Algorithm of atmospheric MTF simulation
The algorithm is given below:

Read the Original Image

Convert it to RGB Image.

Converts and scales input image to specified output data type.

Pad or crop a twodimensional input image.

Apply fast Fourier transform in two dimensions (2D FFT).

Apply MTF equations (aerosol turbulence Both) to the input image.

Apply inverse fast Fourier transform (2D IFFT).

Display the image.


QUALITY ASSESSMENT
Evaluation methods of the remote sensing data quality are generally classified into two types: the subjective evaluation and the objective evaluation [5].
A Subjective fidelity criterion
RMSE is not the only measure to evaluate the reconstructed image quality. Two images could have the same RMSE but would have different visual quality. To solve this problem a subjective fidelity criterion is defined depends on the visual quality of the image evaluated by the Human Visual System (HVS). This can be accomplished by showing a typical decompressed image to an appropriate cross section of viewers and averaging their evaluations [6].
The subjective quality assessment of the image cannot be independent of the vision, but since human vision is not sensitive to the variation of image and its partial that the image vision quality is absolutely depended on the observer, we need to synthetically evaluate the quality associating with the objective quality assessment standard. [7].
B Objective fidelity criterion
The effect of atmosphere is expected to degrade the spatial resolution of the original images. In this paper we use Universal Image Quality Index (UIQI), Discrepancy (D), Root Mean Square Error (RMSE) and Peak Signal to Noise Ratio (PSNR) to assess the spectral quality of the degraded image. The spectral quality of the recovered images will be evaluated by comparing their spectral information with that of respective original one. This comparison is performed quantitatively using the following measures:

Universal Image Quality Index (UIQI) [8]The UIQI is designed by modeling image distortion as a combination of three factors; loss of correlation, radiometric distortion, and contrast distortion. It is defined by the following formula:
(4)
Where is the covariance between the bands of modulated images and the input (original) images, and are
the mean and the standard deviation of the images. The dynamic range of UIQI is [1, 1]. The higher UIQI the better spectral quality of the fused image.

Discrepancy (D) between the original images and the modulated images and it is defined as:
(5)
Where are the pixel values at position (i,j) in the original images and the modulated images respectively. M and N are the numbers or rows and
columns of the image respectively. It is known that the spectral quality of the image increases as (D) decreases [9].
Case (3)
2 Study of atmospheric aerosol effect

Root mean square error (RMSE)
It is the square root of the mean square error between the original and reconstructed image. It detects the difference between the reconstructed and the original image [10].
Particulate radius = 102 cm2 Wave length = .55m.
Absorption coefficient = 1
Note that: the absorption is not a diffraction process and
RMSE
Where:
1 M1 N1
(g(x, y) f(x, y))2 (6)
M N x 0 y0
doesnt depend on spatial frequency .because when we set MTF (=0) =1 the absorption effect will be normalized out only the scattering process is important for development of an aerosol MTF. [6]

f(x,y) The original or input image.

g(x,y) The output image (the reconstructed image after the compressiondecompression process).

M x N . The image size.


Peak signal to noise ratio(PSNR)
Peak signal to noise ratio is defined as [10, 11]:
PSNR=
X 2
10 log[ max ]
Scattering coefficient = 2 (Thin fog) Case (4)
Particulate radius = 102 cm2 Wave length = .55m.
Absorption coefficient = 1.
Scattering coefficient = 78.2 (dense fog)
Here we use a data set from 5 images with different resolutions as in table (3) the results in table (4).
3Study of the overall atmospheric MTF effect
1 M 1
N 1
(g(x, y) f (x, y))2
MxN
x0
y0
[dB] (7)Case (5) [weak turbulence + clear weather]
Under the following conditions
Where, Xmax is the maximum gray level (255 for 8bit level) of the given input image. In case of multispectral satellite images the PSNR is multiplied by the number of image bands
.The PSNR is more commonly used than the RMSE, because people tend to associate the quality of an image with a certain range of PSNR. Table 3 illustrates the PSNR values and its indication [12].


EXPERIMENTAL RESULTS AND CONCLUSION: 1Study of atmospheric turbulence effect
Case (1)
Range = 700 km. Wave length = .55m.
Index structure parameter =1015m2/3. (Weak turbulence)) Case (2)
Range = 700 km. Wave length = .55m.
Index structure parameter =5×1013m2/3 . (Strong turbulence(
Range = 700 km. Wave length = .55m.
Index of refraction =1015m2/3. (Weak turbulence) Particulate radius =102 cm2
Wave length = .55m. Absorption coefficient = 1
Scattering coefficient = .19 (clear)
Case (6) [strong turbulence + dense fog] Under the following conditions
Range = 700 km. Wave length = .55m.
Index of refraction =5×1013m2/3. (Strong turbulence) Particulate radius = 102 cm2
Wave length = .55m. Absorption coefficient = 1
Scattering coefficient = 78.2 (dense fog)
We apply the different cases above to a different images with a different spatial resolution acquired from different sensors all with size 512×512 pixels illustrated in table 5. and Table 6 show the results of applying the spatial and the spectral quality metrics on the images.

CONCLUSION
The aerosol MTF is dependent on the aerosol size distribution, absorption and scattering coefficients. Furthermore, the aerosol MTF actually recorded in the image is usually affected also by the optical and photoelectronic instrumentation, the aerosol MTF prediction is concentrated on predicting the aerosol size distribution that often performed by LOWTRAN and its successor MODTRAN which give very good results in the prediction of absorption attenuation according to the weather The distribution of coarse particles changes very sharply with weather. They give rise to smallangle forward light scattering and cause image blur.
The turbulence MTF is dependent on the index structure parameter Cn2 to predict it, notable computer programs such as IMTURB and PROTURB have been developed by scientists at the U.S. Army Atmospheric Sciences Laboratory. [13]
Aerosol blur, often referred to as the adjacency effect, is well established as the primary and perhaps only source of atmospheric blur in remote sensing imaging from satellites. However, much of the propagation community considers turbulence blur only in interpreting experiments, and then notes discrepancies with turbulence theory without considering how broad system engineering approach is called for, which includes aerosols, turbulence and many other atmospheric effects. In general, turbulence is most significant at low elevations up to a few meters above earth's surface, and aerosol blur is most significant at higher elevations. [14]
REFERENCES

I. Dror and N. S. Kopeika, Experimental comparison of turbulence modulation transfer function and aerosol modulation transfer function through the open atmosphere J. Opt. Soc. Am. A/ Vol. 12, No. 5/May 1995.

N. S. Kopeika and D. Arbel, Imaging through the atmosphere: an overview, SPIE 1999 Vol. 3609.

Ronald G. Driggers, Paul Cox, Timothy Edwards "Introduction to Infrared and ElectroOptical Systems". Artech House Optoelectronics Library, Artech Print on Demand (1998).

Raviv Melamed, Yitzhak Yitzhaky, Norman S. Kopeika, Stanley R. Rotman " Experimental comparison of three target acquisition models" BenGurion University of the Negev Department of Electrical and Computer Engineering. (1998)

Lidong Guo, Guoqing Li" Research on method of remote sensing data quality contrast among different quantization levels"International Conference on Remote Sensing, Environment and Transportation Engineering (RSETE 2013).

Rafel C.Gonzalez , Richard E.Woods, "Digital Image Processing",
Second edition, university of Tennesse, MedData Interactive, 2002

Yijian Pei, Jiang Yu The Improved Wavelet Transform Based Image Fusion Algorithm and The Quality Assessment .3rd International Congress on Image and Signal Processing (CISP2010).

Ayman H. Nasr, Mohamed R. Metwalli Comparative Performance of the Integration of ETM8 and ERS1 Data for Geological Application
International Journal of Computer Applications (0975 8887) .2014

M. Fallah Yakhdani , A. Azizi QUALITY ASSESSMENT OF IMAGE FUSION TECHNIQUES FOR MULTISENSOR HIGH RESOLUTION SATELLITE IMAGES .ISPRS TC VII Symposium July 57, 2010.

AL Bovik, "Hand book of Image and Video Processing", Department of Electrical and Computer Engineering, The University of Texas at Austin, 2000

M. Mrak, S. Grgi, M. Grgi. Picture Quality Measures in Image Compression Systems. In Proceedings of the Eurocon 2003 conference,
p. 233237, Ljubljana, Slovenia .(2003)

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Yitzhak Yitzhaky, Norman S. Kopeika and I. Dror " Restoration of atmospherically blurred images according to weatherpredicted atmospheric moulation transfer functions" BenGurion University of the Negev Department of Electrical and Computer Engineering. 1997

Norman S. Kopeika " Blur in imaging through the atmosphere: a system engineering approach to imaging" SPIE 3433, Propagation and Imaging through the Atmosphere II. 1998
TABLE I. ATMOSPHERIC PARTICLES
Atmospheric particles 

Particle type 
Radius (m) 
Density (per cmÂ²) 
Air molecules 

Haze particles 

Fog droplet(mie) 
110 
10100 
Raindrops (Geometric) 
TABLE II. SCATTERING COEFFICIENT (FROM HOLST)
Scattering coefficient (from Holst) 

Condition 
Scattering coefficient 
Dense fog 
78.2 
Moderate fog 
7.8219.6 
Thin fog 
1.963.92 
Haze 
.981.96 
Clear 
.19.39 
Very clear 
.078.19 
TABLE III. STANDARD EVALUATION OF SUBJECTIVE METRICS
Standard evaluation of subjective metrics 

Scores 
Quality scale 
Obstruction scale 
5 
Very good 
Audiences can seldom find the image quality deterioration 
4 
Good 
Audiences can find the image quality deterioration, but it doesnt impede watching 
3 
Ordinary 
Audiences can clearly find the image quality deterioration, and it impedes watching slightly 
2 
Bad 
It impedes watching 
1 
Very bad 
It impedes watching very seriously 
TABLE IV. THE PEAK SIGNAL TO NOISE RATIO AND ITS DESCRIPTION
The Peak Signal to Noise Ratio and its description 

PSNR 
Radius (m) Density (per cmÂ²) 
Over 40 dB 
Excellent image (i.e., being very close to the original image). 
Between 30 to 40 Db 
Good image (i.e., the distortion is visible but acceptable). 
Between 20 and 30 dB 
Acceptable. 
Lower than 20 dB 
Unacceptable. 
TABLE V. DATA SET USED
Atmospheric particles 

Sensor 
Resolution 
Place 
GeoEye1 
.5M 
Arizona USA 
Ikonos 
1M 
Vancouve rCanada 
Spot 5 
2.5M 
Shanghai China 
RapidEye 
5M 
Hawaii 
COUNS 
10M 
Hawaii 
TABLE VI. RESULTS
Atmospheri c Effect 
Resolution 
Objective fidelity criterion 

UIQI 
D 
MSE 
PSNR 

Case (1) 
.5M 
1 
0 
0 
99 
1M 
1 
0 
0 
99 

2.5M 
1 
0 
0 
99 

5M 
1 
0 
0 
99 

10M 
1 
0 
0 
99 

Case (2) 
.5M 
1 
0 
02 
99 
1M 
1 
0 
0 
99 

2.5M 
1 
0 
0 
99 

5M 
1 
0 
0 
99 

10M 
1 
0 
0 
99 

Case (3) 
.5M 
0.999987 
0.06418 
0.0991 
8.17 
1M 
0.99998025 
0.112545 
0.1983 
55.15 

2.5M 
0.99996915 
0.124302 
0.2616 
53.95 

5M 
0.999986 
0.01667 
0.0828 
58.94 

10M 
0.999993 
0.00509 
0.0334 
62.88 

Case (4) 
.5M 
0.989148 
3.68901 
141.4748 
26.62 
1M 
0.98802657 
6.71129 
299.9488 
23.360 

2.5M 
0.981576 
7.39157 
344.2767 
22.76 

5M 
0.977649 
4.96517 
180.7632 
25.55 

10M 
0.991651 
3.48528 
99.6658 
28.14 

Case (5) 
.5M 
1 
0 
0 
99 
1M 
1 
0 
0 
99 

2.5M 
1 
0 
0 
99 

5M 
1 
0 
0 
99 

10M 
1 
0 
0 
99 

Case (6) 
.5M 
0.989148 
3.68901 
141.4748 
26.62 
1M 
0.988026 
6.71129 
299.9486 
23.36 

2.5M 
0.981576 
7.39157 
344.2764 
22.76 

5M 
0.977649 
4.96517 
180.7631 
25.55 

10M 
0.991651 
3.48528 
99.6658 
28.145 
Fig (1) Algorithm of atmospheric MTF simulation