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Multiple Feature Analysis for Infrared Small Target Detection

Detection of small target has been an important and challenging task in infrared systems. Most detection algorithms which only use single metric are difficult to separate target from clutter completely. The false alarm may be high when there exists complex backgrounds. In this letter, multiple novel features are proposed from four aspects to establish elaborate description. Each feature reflects specific characteristic of small target. The best feature vector is selected to apply these features for detection. Then, learning-based classifier is trained to screen candidate targets which are obtained by initial segmentation. Experimental results demonstrate that the proposed features could discriminate small targets from various clutters effectively. The better detection performance is achieved compared with other methods in different infrared backgrounds.