Polarization has been shown to improve object-clutter discrimination in longwave infrared imaging, particularly if the object and clutter have the same apparent surface temperature and the viewing angle relative to an object's surface is off normal. This work describes experimentation to investigate the feasibility of using polarimetric infrared imagery to enhance object-clutter discrimination when the object is hidden by foliage. Many obscurations have small gaps where optical signatures from background objects can be partially seen. In long range imaging, large pixel size typically creates heterogeneous pixel mixtures consisting of multiple material surfaces. This mixture degrades an object's signature; however, due to the significant polarization contrast from the materials, object-clutter discrimination is still possible. Methodology and results from controlled experiments are presented herein which demonstrate the potential capability of object detection using polarization sensitive imagery.
An experiment is conducted to observe painted aluminum panels across long-wave, mid-wave, and short-wave regions of the optical infrared spectrum with respect to time. Simultaneously, comprehensive meteorological information including solar intensity, temperature, humidity, and moisture are also recorded. The experiment is focused on 1) understanding the cause of signature variability of several objects in the scene in relation to numerous meteorological conditions, 2) observing the potential benefit offered in passive polarimetric sensing, and 3) identifying the strengths and limitations of each waveband for the encountered condition. Metrics include intensity, polarization, and contrast from multiple wavebands measured across various solar conditions against painted panels and natural clutter. We present details of the experiment setup, analysis of imagery and meteorological data, and observations drawn from experimental results.