Hybrid System for Ship Detection in Radar Images
This paper describes a research activity devoted to verify the feasibility of a neural network approach for automatic detection of naval targets in radar imagery. The activity is part of a more ambitious industrial project concerning the use of advanced image processing techniques for improving the safety in maritime surveillance of harbour traffic. Having in mind this final application, the task of automatic target detection has to fulfil two main requirements: 1) quasi real time response, in the sense that each input radar image of about 1000 by 1000 pixels has to be analyzed in a time comparable with the sweep period of the acquisition system, i.e. three seconds; 2)very high accuracy, i.e. each ship in the scene must be surely detected: this means that the efficiency of the detection system should be not minus than 100%. These two applicative needs have to meet with the high degree of noise (clutter) characterizing the input radar data. In order to face up to the complexity of the exposed goal, the task has been decomposed in a set of sequential subtasks: noise elimination, preliminary object recognition and classified image reconstruction. Moreover, both neural and traditional tools have been used, designing a multi-modular hybrid system (a detailed discussion about the advantages of hybrid architecture can be found, for example, in (F. Folgelman Suolie, 1993)).
KeywordsRadar Image Port Area Real Time Response Noise Elimination Radar Imagery
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