A new polarization image fusion method based on Choquet fuzzy integral

Polarization imaging provides abundant information of object, i.e. surface roughness, texture, physical and chemical characters. Independently, intensity and polarimetric features give incomplete representations of an object of interest. These representations are complementary, and it is expected that the combination of complementary information will reduce false alarms, improve confidence in target identification, and improve the quality of the scene description. Polarization parameter images include the degree of polarization, the angle of polarization, azimuth angle etc. There are not only strong correlations between polarization parameter images, but also different characters, which gives image fusion challenges, namely, how to find the optimal polarization parameter image to take part in image fusion with intensity image. This paper presents a polarization image fusion method based on choquet fuzzy integral. Using this algorithm the best polarization parameter image and intensity image are fused, and the fusion result is evaluated. The experiments show that this method could automatically select the best polarization parameter images from multi-polarization parameters image, the resulting images can yield more detail and higher contrast, and can reduce the noise effectively. It is conducive to the subsequent target detection.