Full text: Mapping without the sun

ON-ORBIT MTF ESTIMATION METHODS FOR SATELLITE SENSORS 
LI Xianbin, JIANG Xiaoguang, Tang Lingli 
Academy of Opto-Electronics, Chinese Academy of Sciences, Beijing, 100080, China 
lixianbin@aoe.ac.cn, Tel: (86)-10-62641267, Fax: (86)-10-62643022 
KEYWORDS: Modulation transfer function (MTF); Point source/array method; Pulse method; Knife-edge method; Bi-resolution 
method 
ABSTRACT: 
Modulation transfer function (MTF) is a standard measurement of imaging systems’ geometric performance. Prior to flight, the MTF 
of satellite sensors is strictly measured in laboratory through various targets. However, it is important to estimate the MTF of satellite 
sensors during their life cycles to determine if any system degradations occur over time. Therefore, almost all satellite management 
organizations put much emphasis on MTF estimation and analysis. However, remote sensing data quality analysis and control in 
China is not emphasized until recent years. As part of Sky-To-Earth System of Systems, we systematically research MTF estimation 
methods and development on-orbit payload MTF test module. This paper first reports the principle of several widely-used on-orbit 
MTF estimation methods (including point source/array method, knife-edge method, pulse method, and bi-resolution method), their 
target deployment/selection standards, data processing steps, and their advantages and drawbacks. Then, we give an example of MTF 
estimation result using knife-edge method and pulse method. 
1 INTRODUCTION 
The spatial resolution of satellite-borne sensors is usually 
described by the Modulation Transfer Function (MTF), i.e. the 
Fourier Transform (FT) of the impulse response (system’s 
response to an ideal point source or Point Spread Function 
(PSF)). Prior to flight, the MTF of satellite sensors is strictly 
measured in laboratory through various targets. However, this 
important parameter for image quality has to be checked 
on-orbit to be sure that launch vibrations, space condition when 
imaging, the performance variation of detectors and other 
components have not spoiled the sharpness of the images. 
Therefore, it is important to estimate the MTF of satellite 
sensors during their life cycles to determine if any system 
degradations occur over time. 
Moreover, Remote sensing is complex information acquire 
process, and each step will introduce some degradation to the 
acquired image, including ground sampling, atmosphere scatter 
and absorption, remote sensors’ effect (diffraction, aberrations, 
focusing error, charge diffusion, platform motion, et al), image 
transmission, ground image process, and so on. All these 
degradations’ cumulative effect during image acquisition and 
transmission is described by system overall Modulation 
Transfer Function (MTF) which can be estimated from remote 
sensing imagery. Therefore, MTF is useful to make a 
deconvolution filter whose purpose is to enhance image 
contrast by ground processing. 
There have many on-orbit MTF estimation methods been 
studied and implemented. Usually, we divide those methods 
into point source/array method, knife-edge method; pulse 
method according to their targets. Furthermore, if we can 
acquire image couples of the same landscape in the same or 
similar spectral band with two different spatial resolutions, the 
higher resolution image can stands for the landscape so that the 
ratio of the image spectra gives the lower resolution sensor 
MTF. This is so-called bi-resolution method. Thus, this paper 
introduces their target deployment/selection standards, data 
processing steps, and their advantages and drawbacks. Then, 
we give an example of MTF estimation result using knife-edge 
method and pulse method. 
2 POINT SOURCE/ARRAY METHOD 
2.1 The principle of point source/array method 
For line position (space) invariant system, the image produced 
by image system g(^,^) can be represented by the convolution 
of system response psF(x,y) ar| d input scene f(x,y) > given by: 
g(x,y) = f(x,y) * PSF(x,y) + n(x,y) 
Where n(x,y) is system introduced noise. According to 
Convolution Theorem, the equation can be expressed in 
frequency domain as: 
G(w.v) = F{u, v) • OTF (u, v) + N(u, v) 
Where g(w.v) , F(«,v) » OTF{u,v) , N(u,v) are the Fourier 
transform of g ( x ,y) , f(x,y) > PSF(x,y) , n(x,y) respectively. 
According to optical theory, MTF is the modulus of OTF 
(called Optical Transfer Function). 
Therefore, if we deploy a spotlight or convex properly, the 
system’s response to the point source can be treated as Point 
Spread Function (PSF). However, limited by system’s spacing 
interval, the imaging system’s response to point source is 
undersampled, which leads to aliasing effects on the MTF 
when it is computed directly using Discrete Fourier Transform. 
The cheapest way to overcome aliasing problem is to use a 
model. Another widely-used way is to properly deploy an array 
of point source with different sample phase, and align these 
system’s responses to a common reference according to the 
sample phase to get oversampled PSF. 
Figure 1 illustrates that system’s response to point source with 
different phase get quite different response (see a-d), aligning 
those responses according to peak locations gets oversampled 
1-D system’s response (see e).
	        
Waiting...

Note to user

Dear user,

In response to current developments in the web technology used by the Goobi viewer, the software no longer supports your browser.

Please use one of the following browsers to display this page correctly.

Thank you.