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Proceedings, XXth congress

S.Mohammad Shahrokhy
Iran's Space Agency- Tehran — Iran
s m sh(ghotmail.com
KEYWORDS: Atmospheric, Radiometric, Geometric, QualityDiagnosis, Assessment, Improvement Elimination
Remotely sensed images are interpreted pixel by pixel, using spectral vector analysis methods. Most kind of noise and perturbation in
pixel value or position cause misinterpretation. In this paper most common Radiometric, Atmospheric and Geometric defects of
remotely sensed images are investigated along with the diagnosis and elimination methods on some high and medium resolution satellite
images. Quality assessment isperformed in both visual and statistical manner and also qualitymprovement is fulfilled in both Manual
and Automatic ways. Many technical methodsare used such as histogram transformation, mean, variance and median calculation of lines
and bands, spatial filtering, template matchingyectifications using GCPs and brghtness temperature and reflectance checking. Visual
diagnosis of defects isoften more precise but not appropriate for automatic procedures. Manual elimination of the defects is also more
accurate however time consuming and user dependent.

; : ; bass ; uality Defect Visual Diagnosis Statistical
Reliable interpretation and results necessitateinput data Quality Q y > Diagnosis
Assessment (QA) and sometimes Quality Improvement (QD). Striping Different overall Significandy
On the other hand, in automatic procedures, image Quality
should be checked to accept or reject the input or sometimes
improve itto be able to cope with the expectedduty.
Remote sensing image Quality generally has three aspects
Radiometric Quality, Atmospheric Quality and Geometric
Quality. Radiometric Quality is affected by sensor
Le eo . S c € right € SITIC
characteristics and detector responses. Striping Drop lines, Noise Dark amd bright Radiometric
. n EM eL 3 es points at the anomalies
Noise and Band missing are of this sort. AtmosphericQuality is
; ; ] ; background
dependent on the circumstances at the imaging time. Cloud T = 7
= as ; n° he Band Missing Lack of data in a Zero variance of a
cover and Haze are of this type. Geometric Quality is either band Band
at an
dependent on sensor characteristics and also satellite sitation
such as attitude, position, velocity and perturbations. Earth}
surface relief is another important factoraffecting Geometric
Quality of the image. Band to band Misregistration and image
to map Misregistration areof geometric Quality elements (QE)
[t is essential to note that each sensor has special Quality
Assessment and Quality Improvement methods, thresholds and
coefficients So images of each sensor must be processed
separately. In this research, TERRA-MODIS, NOAA-AVHRR,
IRS-PAN and IRS-LISS III images are investigated.
Many works have been done on image Quality control
(Barrett 1990, Nill & Bouzas 1992, Eskicioglu & Fisher 1995,
Barrett 1995, Westen et al 1995, Taylor 1998,
Avicibasand Sankur 2000) and generally each company
provides a compbte report of its sensor imags and products
Quality e.g. EOS (Chu et al 2000 , Vermote et al 1997).
2.1 Radiometric Quality Assessment
Radiometric Quality elements and recognition methods are
briefly listedin Table 1.
brightness of
adjacent lines
different variance
and mean of
adjacent lines

Drop Line
Null scan line
Zero variance of a

Table 1. Radiometric Qualitydefects and diagnosis methods
Striping is caused by different response of elements of a
detector array to same amount of inoming EM energy. This
phenomenon causes heterogeneity in overall brightness of
adjacent lines figure 1).

Figure I. Image No.l (MODIS) with stripes

in à