Material Clustering Using Passive Millimeter-Wave Polarimetric Imagery

Abstract

Passive millimeter-wave imaging has emerged as a useful tool in many remote sensing applications, including resource remote sensing, material classification, and target detection. This paper presents a method to classify specular objects based on their material composition from passive millimeter-wave polarimetric imagery. Passive degree of polarization (PDoP) is proposed and calculated by using the results of a passive millimeter-wave polarization measurement. The PDoP values of typical ground targets are further analyzed. Outdoor experiments are conducted, and the PDoP image is generated from the brightness temperature images. The PDoP values of pixels in the image are statistically analyzed, and the threshold values are set based on the statistical analysis results, then every pixel is recognized. Experimental results indicate that this method is highly effective for distinguishing among various materials of interest.