False alarm sources detection based on LNIP and local probability distribution in infrared image

Abstract

Given that the infrared radiation energy of false alarm sources such as rivers, high-altitude cirrus clouds and icy lakes is similar to that of the infrared targets, the false alarm sources seriously affect the accurate detection and tracking of the target by Infrared Search and Track (IRST) system. To reduce the false alarm rate in infrared target detection, a pixellevel algorithm is proposed in this paper to detect and eliminate false alarm sources. Firstly, we use histogram equalization and gray stretch to enhance the infrared image. The next step is to locate the false alarm source based on Local Neighborhood Intensity Pattern (LNIP) and local probability distribution, which is critical in false alarm source detection. Finally, we use region growing based on fused saliency feature to strengthen the accuracy of the location. Numerous experimental results manifest that the algorithm proposed in this paper boasts a detection rate of more than 96% for multiple false alarm sources, and performs better on the Precision-Recall (PR) curve and the Receiver Operating Characteristic (ROC) curve among the algorithms based on texture analysis.