Theoretical Research on Large Field-of-View Polarization Imaging Based on Dynamic Vision Sensors

The combination of dynamic vision sensors (DVSs) and polarization can overcome the limitation of DVSs whereby they can only detect dynamic scenes, and it also has the ability to detect artificial targets and camouflaged targets, and is thus expected to become a new means of remote sensing detection. Remote sensing detection often requires the field-of-view (FOV) and width to be large enough to improve detection efficiency, but when large FOV polarization imaging is performed, the polarization state in the edge FOV and the center FOV will not be consistent, which does not meet the paraxial approximation condition, and the inconsistency increases as the angle between the incident light and the optical axis increases. This affects the accuracy of target detection, so in this paper, based on the characteristics of polarization imaging using a DVS, factors such as the polarizer rotation step, incident light polarization state, and incident angle are considered to establish a theoretical model of large FOV polarization imaging using DVSs. And the influence of the detection ability is analyzed for three types of incident conditions, namely linearly polarized light, natural light, and partially polarized light. The results show that when the rotation step is 5°, the highest false alarm rate for natural light incident in the edge FOV will be nearly 53%, and the highest false alarm rate for linearly polarized light incident will be nearly 32%.