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.
