Relative
radiometric normalization (RRN) minimizes radiometric differences among images
caused by inconsistencies of acquisition conditions rather than changes in
surface. Scale invariant feature transform (SIFT) has the ability to
automatically extract control points (CPs) and is commonly used for remote
sensing images. However, its results are mostly inaccurate and sometimes
contain incorrect matching caused by generating a small number of false CP
pairs. These CP pairs have high false alarm matching. This paper presents a
modified method to improve the performance of SIFT CPs matching by applying sum
of absolute difference (SAD) in a different manner for the new optical
satellite generation called near-equatorial orbit satellite and multi-sensor
images. The proposed method, which has a significantly high rate of correct
matches, improves CP matching. The data in this study were obtained from the
RazakSAT satellite a new near equatorial satellite system. The proposed method
involves six steps: 1) data reduction, 2) applying the SIFT to automatically
extract CPs, 3) refining CPs matching by using SAD algorithm with empirical
threshold, and 4) calculation of true CPs intensity values over all image’ bands, 5) preforming a linear regression model
between the intensity values of CPs locate in reverence and sensed image’ bands, 6) Relative radiometric normalization conducting using regression
transformation functions. Different thresholds have experimentally tested and
used in conducting this study (50 and 70), by followed the proposed method, and
it removed the false extracted SIFT CPs to be from 775, 1125, 883, 804, 883 and
681 false pairs to 342, 424, 547, 706, 547, and 469 corrected and matched
pairs, respectively.
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