PoLP-ICOP: Cross-validation of Power of Linear Polarization and Infrared CRI-based Optimal Polarization for artificial object detection

Abstract

It is important to detect man-made objects in a natural background to reduce false detections in long-wave infrared for safety and security applications. The degree of linear polarization (DoLP) is used frequently to solve such problems. DoLP can provide important clues for man-made object signatures. On the other hand, DoLP cannot handle the polarization power because of normalization. First, a novel physics-driven power of linear polarization (PoLP) metric is proposed to find optimal infrared polarization conditions analytically. Second, a data-driven infrared polarization method is presented. Few studies have been conducted in terms of polarimetric optimization at a low level. This paper presents a novel polarimetric information utilization method by applying a two-layered neural network with the inverse contrast radiant intensity (CRI) loss function to find physical meaning. The proposed infrared CRI-based optimal polarimetry (ICOP) could extract the low-level contribution of each polarimetric image in discriminating artificial objects in a natural background. After optimization, the learned weights of the polarimetric images were sine-like, which produced optimal object and background separation. The experimental results for the outdoor scenario validated the optimality of the proposed ICOP in man-made object detection in a natural background. Finally, the physics-driven PoLP coincided with the data-driven ICOP in man-made object detection.