components. A high degree of correlation was observed between the 
bands (Table II). The results of applying the KL transformation 
using the eigenvectors were observed by the seven prineipal 
components. Of these the last three components (5-7) were found to 
contain mostly noise. Further confirmation of this and the 
efficient compaction is evident from Table III. This table gives a 
comparison of variances of the original and transformed 7 bands. 
Note the very low variance in the transformed bands 5-7 indicating 
noise. 
Principal components 5-7 were dropped in the reconstruction (inverse 
KL) process. In other words they were set to 0.0 . The results of 
the forward-inverse KL transformation with components 5-7 dropped 
indicate reduced noise level. Due to limited space, all the 
original and reconstructed images are not shown here.’ Figures 1-3 
show zoomed areas over water in bands 1, 2 and 7. Note the reduced 
detector-to-detector and band noise. Most of the errors in 
difference images were -1, 0 or +1. There were very few pixels with 
absolute errors greater than 2. 
Table IV shows the various quantitative measures, MSE, mean of 
absolute error (MAE), peak-to-peak signal to noise ratio (SNR) and 
the correlation coefficient. Measures were computed for all seven 
bands of the reconstructed image with the original as the reference. 
Note that the MAE for any single band does not exceed 1.6. The 
results agree with the observation by other researchers that the 
stripe noise and band noise introduce errors that are in this range 
of gray level or digital number (DN) [3,4,18]. Also note the very 
high correlation and the SNR. 
Instead of retaining the high variance principal components (1-1) in 
the reconstruetion process, the low variance components (5-7) can be 
retained. Such a reconstructed 7-band data will enable to observe 
what is being stripped from the original bands by the noise removal 
process. One expects to see mostly the noise in these reconstructed 
images. The stripe noise and band noise are clearly visible in 
these images. Figures 4-6 show reconstructed bands 1, 2 and 7 with 
the high variance principal components 1-4 set to 0.0 instead of 5-7 
being set to 0.0. The images have been scaled for better visual 
effects. As expected, note that these figures show strongly the 
banding, stripes and very little image structure. 
Conelusions 
Noise removal in TM images has been shown through spectral filtering 
using the KL transform. The results are very encouraging. The 
processed images are expected to yield better results for 
classification in land use, crop inventory, forestry studies etc. 
Additionally since the process is a spectral filter, a failed 
detector response or a dropout is inherently rectified by the 
process. It has been found by other researchers that interpolation 
of failed detector values from adjacent bands is better than using 
ad jacent detector values from the same band. The KLT processing 
replaces the failed detector values by a weighted average of the 
same detector from all the bands. The results have been found to be 
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