1 
IMAGE-BASED ESTIMATION AND VALIDATION OF NIIRS FOR HIGH-RESOLUTION 
SATELLITE IMAGES 
Taejung Kim 3 ' *, Hyunsuk Kim 3 and HeeSeob Kim b 
3 Dept, of Geoinformatic Eng., Inha University, Republic of Korea, tezid@inha.ac.kr, cfmove@naver.com 
b Korea Aerospace Research Institute, askhs@kari.re.kr 
Commission I, Working Group 1/1 
KEY WORDS: GSD, MTF, SNR, NIIRS, GIQE, Edge Response 
ABSTRACT: 
As high resolution satellite images are being used widely in many applications more and more users are demanding images of good 
quality. The ‘quality’ of satellite images are expressed by many technical terms such as ground sampling distance, modular transfer 
function, and signal to noise ration and by NIIRS (National Imagery Interpretability Rating Scale) in user community. The purpose 
of this study is to develop techniques to estimate NIIRS of images through image analysis and using the GIQE (General Image 
Quality Equation). We assessed NIIRS values by human operators for various high resolution images. We then used GIQE and 
estimated NIIRS values through image analysis. We compared the NIIRS values obtained through image analysis with the values 
from human operators and with the NIIRS values provided in the image metadata. Results showed that the NIIRS values provided in 
the metadata were larger than the values estimated by human operator. This could mean that the value in the metadata assumes ideal 
conditions and the exact cause of this difference is under current investigation. The NIIRS values estimated through image analysis 
were lower than the values estimated manually. However, they showed the same pattern as the NIIRS values estimated manually. 
This indicates that the NIIRS values estimated though image analysis using the GIQE can represent actual interpretability of the 
image. This also indicates that if we can provide edge points automatically we may achieve fully automatic estimation of NIIRS 
values. The contribution of this study is that we proved the reliability of image analysis methods for calculating NIIRS values and 
showed the possibility of an automated technique of estimating NIIRS from images so that the value of NIIRS is systematically 
calculated at satellite ground stations. 
1. INTRODUCTION 
High resolution satellite images are being used widely in many 
applications as the number of operational high resolution 
remote sensing satellites has been increasing rapidly. In 
particular the level of satellite images has reached to that of 
aerial images in terms of ground sampling distances. The 
resolution of images taken from Worldview, for example, is less 
then a half meter. As satellite images became popular users are 
demanding ‘good’ or ‘better’ images. However what do they 
mean by ‘good’? 
The ‘quality’ of satellite images are expressed by many 
technical terms such as ground sampling distance (GSD), 
modular transfer function (MTF), and signal to noise ration 
(SNR). However, these parameters can only indicate 
interpretability partially. GSD, which tells the spatial resolution 
of images, is probably the most popular parameter and the most 
important one. However it is not an ultimate parameter to 
describe ‘quality’ of images. Images with same GSD, for 
example, may have very different interpretability. MTF and 
SNR can specify only some aspects of image quality. Besides, 
these parameters are used mostly in technical fields and 
technical people such as satellite manufacturers, optical 
engineers or electric engineers. Image users may not understand 
the exact meaning and moreover they will not understand easily 
how good images will be with GSD, MTF and SNR numbers. 
For this reason, NIIRS (National Imagery Interpretability 
Rating Scale) has been proposed as a measure of image quality 
* Corresponding author. 
in terms of interpretability (IRARS, 1996). NIIRS describes 
interpretability of images by numbers ranging from 0 to 9. At 
each level, NIIRS defines objects that should be able to observe 
within images. NIIRS defines observation objects for military 
targets originally and it extends the definition of observation 
objects for man-made and natural targets. For example, at 
NIIRS level 4 we should be able to detect basketball court, 
tennis court and valley ball court in urban areas and at NIIRS 
level 5 identify tents larger than for two persons at established 
recreational camping areas and to distinguish between stands of 
coniferous and deciduous trees during leaf-off condition 
(IRARS, 1996). For satellite images at lm GSD, NIIRS level of 
4.5 is known to be nominal. 
NIIRS is to be estimated by human operators. In users point of 
view NIIRS is probably the best measure of determining the 
goodness of images with respect to interpretability. For this 
reason, NIIRS numbers are provided with high resolution 
images such as Quickbird as a part of the metadata. 
Research has been carried out to relate technical quality 
measures such as GSD, MTF and SNR to application quality 
measure such as NIIRS. As a result general image quality 
equation (GIQE) was proposed (Leachtenauer et al., 1997). 
GIQE estimates NIIRS from GSD, edge response, which is 
related to MTF, and SNR. Using this equation, one can estimate 
the interpretability or goodness of images from technical terms. 
The purpose of this study is to develop techniques to estimate 
NIIRS of images through image analysis and using GIQE.