radiometric preprocessing steps which are 
described in more detail in the following 
sections. 
Sensor Performance 
The PMI sensor consists of five cameras, each with 
an area detector array consisting of 385 pixels by 
288 colour elements (bands). Each of the cameras 
provide eight profiles for which each profile 
element is an average over 10 spatial pixels with 
a 38-pixel gap between profiles. Due to problems 
with the first camera during the overflight, the 
data from this camera could not be incorporated 
for further processing. Furthermore, each third 
profile of the remaining four cameras was lost 
with respect to the transcription of the data from 
the high density digital tape to CCTs. The 
remaining 28 profiles, consisting of 773 lines, 
were examined for bad data in the spectral and 
spatial domains. Figure 2 shows some examples of 
spectra with bad data. Bad bands were located in 
profiles 9 (bands 113-137), 14 (279), 16 (281- 
288), and 21 (268) and replaced with the average 
of the adjacent bands. A correction, however, was 
not applied for profiles 9 and 16 which had 5 or 
more contiguous bad bands. These particular bands 
were not included for data interpretation 
purposes. The next step was to replace bad data 
lines within a specific camera by the average of 
the adjacent lines. In addition, the first 20 
bands of each of the remaining profiles were 
eliminated from further processing due to bad 
data, leaving 268 bands for subseguent analysis. 
An estimate of the random noise which is image 
independent resulted in a signal-to-noise ratio 
(SNR) for band 247 (751.21 nm) of 140:1 for a low 
signal level (DN=500; 100.4 x 10" 8 W cm" 2 nm" 1 sr" 1 ) 
and of 1380:1 for a high signal level (DN=15000; 
3013.1 x 10“ 8 W cm" 2 nm" 1 sr" 1 ). Similar SNRs were 
obtained for the other bands. A "laboratory" 
method was used for SNR calculations as described 
in Borstad et al. (1985). Preliminary investiga 
tions indicate that fixed pattern noise could not 
be detected within the image, especially for bands 
with low signal amplitude as reported in the 
aforementioned reference. A further study, 
however, will be conducted in the future using a 
fast Fourier transform for each band to reveal 
potential noise pitch and 'notch filtering' in the 
frequency domain to remove major noise components 
if necessary (Curran and Dungan, 1988; Hlavka, 
1986). 
The central wavelength (band) locations were 
checked using the position of the atmospheric 
absorption features calculated with the 5S 
radiative transfer code (Tanre et al., 1986). 
Major and minor absorption features, for example 
at 590 nm (0 : ,, H 2 0), 690 (H^O, O*), 720 (H*0), and 
760 nm (HzO, Oa), could be detected in terms of 
the wavelength position at 589.53 nm, 687.32, 
718.61, and 760.34 nm for the different PMI 
cameras. The disagreement in the location of the 
absorption features is due to the minimum 5 nm 
wavelength interval allowed in 5S compared to a 
1.3 nm sampling interval for the PMI sensor. 
Calibration 
An integrating sphere is used as a standard 
calibration source for the PMI sensor, resulting 
in a set of multipliers used to convert DN values 
into radiances in terms of W cm" 2 nm" 1 sr“ 1 . A 
total of 288 radiometric calibration values are 
provided (one for each band) along with the image 
data. Besides the removal of the dark current 
from the PMI data, a uniformity (radiometric) 
correction, assuming detector linearity, was also 
applied for the alignment of the different cameras 
(Borstad et al., 1985). 
The compensation for the different sensitivities 
of the cameras was not fully achieved with the 
uniformity technique because the detector 
responses are, in general, non-linear. Further 
corrections were necessary using a polynomial 
regression approach for a total radiometric 
adjustment of the cameras. This method is based 
on using targets which cover two or more cameras. 
Such a so-called standard target should be homoge 
neous. At least 10 such homogeneous, standard 
targets encompassing the DN range of the scene to 
be corrected were selected in order to generate 
the polynomial equation for each band (Figure 3). 
Thus, a separate correction was applied to each of 
the 16-bit DN levels for a compensation of the 
non-linear behaviour of the DN differences between 
cameras in roost of the bands. A second degree 
polynomial was used to remove the DN differences 
between the 'nadir' camera 3 and cameras 2, 4, and 
5 for each of the bands. Satisfactory results 
could be achieved with this method as shown in 
Figure 4 for a sugarbeet field, located within 
cameras 4 and 5, before and after radiometric 
alignment. The effectiveness of this adjustment 
technique depends on the ability to select enough 
standard targets for the generation of the 
polynomial curve as described in detail by Staenz 
(1990). 
This camera adjustment technique also addresses 
some of the viewing angle effects which are quite 
severe for a scan angle range of approximately 
72.5 degrees. This effect is demonstrated in 
Figure 5 using corn data acquired with a 
Spectrascan spectrometer on the ground over a 
viewing angle range of ±24.3 degrees. The varia 
tion caused by the viewing angle in one specific 
camera (14.5 degrees FOV), however, could not be 
removed with the correction of the radiometric 
misalignment of the cameras. An assessment of the 
eight profiles within a specific camera show 
various degrees of data variation (especially in 
camera 4), caused not only by the viewing angle 
phenomenon, but also by sensor related effects 
(CCD responsivity). An algorithm will be imple 
mented in the ISDA software package in the future 
to deal with these within-camera effects. 
The removal of scene-related effects, such as 
those due to the atmosphere or viewing geometry, 
are important preprocessing steps for a 
normalization of the image data in an absolute 
sense. Such steps are necessary for a spectro 
scopic analysis of the data, especially to 
incorporate the generated spectra into a database 
for comparison with laboratory spectra, field 
spectra and spectra derived from other imaging 
spectrometers. An absolute normalization 
procedure consisting of an airborne version of the 
5S radiative transfer code was applied to the PMI 
data set (Tanre et al., 1986; Teillet, 1989). 
This procedure begins with a conversion of the raw 
data into radiances using the radiometric calibra 
tion values delivered with the data set. A 
comparison with ground-based spectra of similar 
targets, however, indicate that the resulting 
reflectances are, on average, approximately a 
factor of two too high. The performance of this 
procedure is satisfactory for Airborne Visible/ 
Infrared Imaging Spectrometer (AVIRIS) data as 
demonstrated in Staenz and Goodenough (1989). 
Based on the results usually achieved with the 
atmospheric model and extensive investigations