Materials classification based on spectropolarimetric BRDF imagery

A new classify method based on spectropolarimetric BRDF imagery is proposed. The performances of three different selected features in classifyication results under various weather conditions including sunny sky, cloudy, and dark sky are emphasized. The three selected features are material spectral information, spectropolarimetric information, and spectropolarimetric BRDF information respectively. Support Vector Machine method is used to classify targets in clutter grass environments, then the classify results based on spectropolarimetric BRDF features are compared with the other two features under the three different weather conditions respectively. The results show that the method based on spectropolarimetric BRDF features performs the best among the three, no matter what the weather conditions are, and its advantage shows most evidently especially in the dark sky. Selecting the spectropolarimetric BRDF information as features in the materials classification will enhance the precision at most time, even in the case when the gray values between backgrounds and targets are very near.