Polarimetric image fusion strategies for improving target shape and contrast in remote sensing applications

Images from passive imaging polarimeters are often displayed in terms of their intensity (s₀), degree of linear polarization (DoLP), and angle of polarization (AOP). The AOP and DoLP together generally provide information about shape and surface quality of imaged objects; however, the information conveyed in these images are often difficult to interpret, especially to an analyst unfamiliar with polarization phenomenology. Furthermore, it can be challenging to train machine learning approaches with direct polarimetric information due to the limited availability of application-specific data. Augmenting s₀ images with polarimetric information in the form of contrast and shape enhancement thus allows for the data to remain in the domain of traditional EO-IR intensity imagery that is more familiar for both humans and trained networks. In this work we explore several strategies for effectively fusing polarimetric information into s₀ imagery to improve target shape and contrast for both human and algorithmic consumption, with a particular focus on remote sensing and target detection applications.