LAUSR

Millimeter-wave radiative sensing has been used in several close-range applications, such as security checks, military detection, terrain modeling, etc. Obtaining target information through radiometry is the goal of the above applications. However, there was no method to recover stable material information and complete geometric information through radiometry. The common problem of physically model-based information recovery is the solution to underdetermined equations. In this article, we analyze the multipolarization brightness temperature model and propose the equivalent permittivity (EP) to characterize the material information and simplify the reflectivity model. With the simplified reflectivity model, we solve the underdetermined problem in information inversion and realize the material classification and surface normal vector (SNV) reconstruction. Simulations and experiments are conducted to analyze the error and suitable range of the simplified reflectivity model and to verify the validity and accuracy of our methods of material classification and normal vector reconstruction. The results show that our classification method is suitable for most objects at an incident angle of 20°∼60°, and our SNV estimation method is effective at all incident angles. The possible applications include liquid composition analysis, target detection, road perception, and three-dimensional reconstruction.