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Previous studies introduced, examined, and tested a variety of registration-free transforms, specifically the diagonal, whitening/dewhitening, and target CV (Covariance) transforms. These transforms temporally evolve spectral object signatures under varying conditions using imagery of regions of similar objects and content distribution from data sets collected at two different times. The transformed object signature is then inserted into the matched filter to search for targets. Spatial registration of two areas and/or finding two suitable candidate regions for the transforms is often problematic. This study examines and finds that the average correlation coefficient between the corrected histograms of the multi-spectral image cube collected at two times can assess the similarity of the areas and predict object detection performance. This metric is applied in four distinctive situations and tested on three independently collected data sets. In one data set, the correlation between histograms was taken from an airborne long wave infrared sensor that imaged objects in Florida and tested on registered images modified by systematically eliminating opposed ends of the image set. The other data set examined images of objects in Yellowstone National Park from a visible/near IR multi-spectral sensor. This comparison was also applied to images collected using oblique angles (depression angle of 10°) of objects placed at Webster Field in Maryland. Candidate heterogeneous image areas were compared to each other using the average correlation coefficient and inserted into statistical transforms. In addition the correlations were computed between corrected histograms based on the normalized difference vegetation index (NDVI). Similarly, the analysis is applied to data collected at oblique angles (10° depression angle). The net signal to clutter ratio depends on the average correlation coefficient and has low p-values (p<0.05). All statistical transforms (diagonal, whitening/dewhitening, target CV) performed comparably using the various backgrounds and scenarios. Objects that are spectrally distinct from the backgrounds followed the average correlation coefficient more closely than objects whose spectral signatures contained background components. This study is the first to examine the similarity of the corrected histograms and does not exclude other approaches for comparing areas.
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