Classification of road surface status using a 94 GHz dual-channel polarimetric radiometer

Abstract

In this article, we classify road surface statuses using a Bayesian classification method. This article uses principal component analysis (PCA) that combines a 94 GHz dual-channel polarimetric radiometer. The radiometer is used to investigate the behaviour of the brightness temperature (BT) of different road surface statuses in an open-air laboratory. The aim of this investigation is to characterize four different road surface classes (dry, wet, snowy and icy). Here, the BT (radiothermal emission) characteristics are measured at horizontal and vertical polarizations. For a given database of weather information (including BT, road surface temperature, wind speed, etc.), a PCA subspace is constructed, and the score vectors are classified by solving the Bayesian classification method. As a result, the road surface statuses were found to be well classified by the proposed method in real time.

Acknowledgement

This work was supported in part by the Centre for Distributed Sensor Network at GIST and the Basic Science Research Programme through the National Research Foundation of Korea (NRF), funded by the Ministry of Education, Science and Technology (no. 2011-0004475).