Polarimetric Lidar Feature Selection for Material Classification

Feature selection for polarimetric lidar material classification is explored. Experimental measurements of a diverse sample set are collected and common features emphasizing polarimetric and polarization-insensitive reflectance are calculated. Multiple classifiers are implemented. Results suggest two transmit and two return polarization states maximize classification performance.