Approach to target detection based on relevant metric for scoring performance

Abstract

Improved target detection, reduced false alarm rates, and enhanced timeliness are critical to meeting the requirements of current and future military missions. We present a new approach to target detection, based on a suite of image processing and exploitation tools developed under the intelligent searching of images and signals (ISIS) program at Los Alamos National Laboratory. Performance assessment of these algorithms relies on a new metric for scoring target detection that is relevant to the analyst's needs. An object-based loss function is defined by the degree to which the automated processing focuses the analyst's attention on the true targets and avoids false positives. For target detection techniques that produce a pixel-by-pixel classification (and thereby produce not just an identification of the target, but a segmentation as well), standard scoring rules are not appropriate because they unduly penalize partial detections. From a practical standpoint, it is not necessary to identify every single pixel that is on the target; all that is required is that the processing draw the analyst's attention to the target. By employing this scoring metric directly into the target detection algorithm, improved performance in this more practical context can be obtained.