Aircraft coatings are critical for protecting the substrate material from corrosion, and in some cases, serve to reduce the aircraft’s RADAR cross section. A coating system is generally formed from three layers: a topcoat, primer, and substrate. Exposed substrate or primer, from defects in the topcoat, poses an operational hazard to the aircraft and can shorten a component’s lifetime. Conventional computer vision techniques require intensive image processing algorithms to detect such defects across a wide range of observation angles, variations in incident illumination, and nondefect markings. We introduce a polarimetric imaging technique that can classify topcoat defects that penetrate to the primer or substrate versus superficial surface markings. Results demonstrate that circular and... linear polarization can discriminate metallic and carbon fiber substrates from the dielectric paint and low observable topcoats. To this end, we demonstrate that the technique of using polarimetric imaging may be viable for in-situ robotic inspection of aircraft topcoats. This is followed by our experimental results, which demonstrate material identification on the single-pixel level.