探索了一种结合目标特性和局部背景类别预测的红外小目标检测算法。具体研 究了红外天空小目标检测中屏蔽地物虚警的问题。在复杂的红外场景中，地面物体由于复杂多变造成 的虚警会严重影响系统的探测灵敏度和鲁棒性。如果仅从目标特性入手，难以滤除地物虚警。首先利 用新 Top Hat 变换提取出潜目标。然后，对每个潜目标，一方面利用目标特性获得一种潜目标为真实目标的 可能性度量，另一方面考虑潜目标一定大小的邻域背景，根据对背景类别(天空或者地物)的预测获得 另一种可能性度量。最后,结合两种度量滤除虚假目标。实验表明，相比仅考虑目标特性的算法，本文 算法的探测性能有了很大提升。

A small infrared target detection algorithm which combines target characteristics with class prediction of local background is proposed. The elimination of false alarms in the detection of small infrared targets in sky is studied in detail. In complex infrared scenes, the false alarms caused by complex and changing ground objects may seriously affect the sensitivity and robustness of a detection system. If the target characteristics are used alone, the false alarms caused by ground objects can be filtered difficultly. Firstly, the latent targets are extracted by using a new Top-Hat transform. Secondly, for each of the latent targets, the likelihood of being true targets is obtained from the target characteristics on the one hand, and another likelihood of true targets is obtained from the prediction of the class label (sky or ground) of the neighboring background on the other hand. Finally, both likelihoods are combined to eliminate the false targets. The experimental results show that compared with the algorithm which uses target characteristics alone, the detection performance of the proposed algorithm is improved greatly.