Abstract
Detection and extraction of objects of interest out of monochrome images based on their compactness and isolation are essential in remote sensing systems data analysis. Algorithms under consideration are based on the results of multi-threshold processing, which provides with a set of binary layers. This allows for further morphological processing of isolated objects in each binary layer in order to analyze their geometric characteristics and perform their subsequent selection by geometric criteria. As a result, one can set an adaptive detection threshold individually for each of the selected objects. Using selection allows to significantly reduce the number of false alarms during detection as well as to use lower-level thresholds this way increasing the probability of correct detection of the objects of interest. The results of synthetic test imagery analysis as well as object detection in remote observational imagery demonstrates explicitly the effectiveness of the considered algorithms.
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