Abstract:
Early detection of air threats like aircrafts and missiles is becoming more and more important for advancing attack distance and response speed of modern hi-tech , weapons in defence applications. These targets at longer ranges, typically 70 km or :- more, appear as point or small targets` in ,visible and infrared image sequences.- _> Because of the lack , of apriori information about- target dynamics and structural information such as shape, the detection t of such targets becomes extremely challenging:
Recently °many algorithms for detection of paint and small targets for airborne applications have been reported in the ',literature. `It is found that most, of these ..:, algorithms are unable to - adapt their behaviour to perform robustly in the rapidly changing environment. In addition, the high computational . complexity`. of reported; detection algorithms requires large processing hardware and leaves a little -sco-pe`for real time . implementation. Hence; a novel and robust long range target detection algorithm based on Adaptive . Selective Double Structuring _ element Top-Hat Transform (Adapt-Set-DSTHT) and -maximum entropy criterion is proposed in this -dissertation report. -Experimental results -show high probability of detection and low false alarms even for highly clouded scenario:,;
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The proposed :robust long range target detection algorithm has also,- been -implemented on PowerPC hardware: AltiVec vector processing unit of Power-PC` G4 is used to speed up the processing and lead to real-time detection of targets
