Unique laboratory experiments are conducted using multiple waveband passive polarimetric and active infrared imaging systems to measure the optical signature of a diverse sample set in support of innovative research in material classification. The primary objective of this work is to explore the feasibility of utilizing multiple sensors of varying waveband or modality to enable or improve classification of common materials relevant in remote sensing applications. This objective includes current remote sensing technologies such as passive polarimetric imaging across multiple infrared wavebands, and light detection and ranging (LiDAR) active imaging. Therefore, to fully explore this objective, representative measurements of diverse materials are collected with three passive polarimeters and a LiDAR system. The measurements characterize material properties such as bidirectional reflectivity, directional emissivity, and surface roughness, which can be used for material classification. Typical passive polarimetric classification techniques assume the polarized signature is generated by reflection, and the imaging geometry is known. We propose to utilize both the polarized signature created by reflections as well as self-emission from the material. The reflectivity and imaging geometry estimations are assisted with the inclusion of LiDAR measurements. We present details of the experiment setup, sample set, analysis of imagery, and observations drawn from experimental results. The capability of classifying materials using passive polarimetric and active infrared imaging systems is investigated.