基于偏振特征重构与局部差异度量的空中红外目标检测OA

Under complex sky backgrounds,the low local contrast between small targets and backgrounds reduces the accuracy of infrared intensity detection.Although infrared polarization imaging can effectively improve the local contrast between targets and backgrounds,it also raises the contrast of high-brightness background edges,bringing new challenges to target detection.To solve this problem,an aerial infrared small target detection method based on polarization feature reconstruction and local difference metric weighting is proposed in this paper.Firstly,considering the unique gradient characteristics of small targets,a global background gradient feature suppression matrix for Stokes vector components S1 and S2 is constructed by using image gradient vector information.Secondly,in view of the spatial correlation between S1 and S2,a polarization feature reconstruction method is established to suppress most background clutter while retaining target features.Meanwhile,according to the feature differences among targets,residual background and noise,an improved Variance Change Rate of Local Region(VSL)is proposed to quantify the complexity of local regions,so as to better suppress residual background.Comparative experiments with LCM,TLLCM,IPI,PSTNN and other algorithms are conducted in multiple scenarios.The experimental results show that the Signal-to-Clutter Ratio(SCR),Signal-to-Clutter Ratio Gain(SCRG)and Background Suppression Factor(BSF)of the proposed method are increased by 14.06％,4.79％and 14.43％compared with the suboptimal algorithm,respectively.It can maintain excellent detection performance under various complex backgrounds and possesses strong robustness.