Material classification based on multi-band polarimetric images fusion

Polarization imparted by surface reflections contains unique and discriminatory signatures which may augment spectral target-detection techniques. With the development of multi-band polarization imaging technology, it is becoming more and more important on how to integrate polarimetric, spatial and spectral information to improve target discrimination. In this study, investigations were performed on combining multi-band polarimetric images through false color mapping and wavelet integrated image fusion method. The objective of this effort was to extend the investigation of the use of polarized light to target detection and material classification. As there is great variation in polarization in and between each of the bandpasses, and this variation is comparable to the magnitude of the variation intensity. At the same time, the contrast in polarization is greater than for intensity, and that polarization contrast increases as intensity contrast decreases. It is also pointed out that chromaticity can be used to make targets stand out more clearly against background, and material can be divided into conductor and nonconductor through polarization information. So, through false color mapping, the difference part of polarimetric information between each of the bandpasses and common part of polarimetric information in each of the bandpasses are combined, in the resulting image the conductor and nonconductor are assigned different color. Then panchromatic polarimetric images are fused with resulting image through wavelet decomposition, the final fused image have more detail information and more easy identification. This study demonstrated, using digital image data collected by imaging spectropolarimeter, multi-band imaging polarimetry is likely to provide an advantage in target detection and material classification.