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A morphology-based algorithm has been developed for point target detection in IRST applications. It exhibits comparable detection and false alarm performance to a median filter. The morphology-based algorithm has an efficient computational paradigm based on combinations of simple nonlinear grayscale operations, which makes it ideally suited to real- time, high data rate IRST applications. A detection filter based on morphological background estimation exhibits spatial high-pass characteristics emphasizing target-like peaks in the data and suppressing all other clutter. Example cases are presented which point out the detection performance differences between the morphological and median approaches. Overall performance results were generated in the form of ROC curves for cloud, horizon and sea clutter IRAMMP backgrounds.
Tom et al. (Fri,) studied this question.