The effects of laser reflection angle on radiometric correction of the airborne LiDAR intensity data

Radiometric correction (RC) of the airborne Light Detection And Ranging (LiDAR) intensity data has been studied in the last few years. The physical model of the RC relies on the use of the laser range equation to convert the intensity values into the spectral reflectance of the reflected objects. A number of recent studies investigated the effects of the LiDAR system parameters (i.e. range, incidence angle, beam divergence, aperture size, automatic gain control, etc.) on the results of the RC process. Nevertheless, the condition of the object surface (slope and aspect) plays a crucial role in modelling the recorded intensity data. The variation of the object surface slope and aspect affects the direction as well as the magnitude of the reflected laser pulse which makes significant influence on the bidirectional reflectance distribution function. In this paper, the effects of the angle of reflection, which is the angle between the surface normal and the incidence laser pulse, on the RC results of the airborne LiDAR intensity data is investigated. A practical approach is proposed to compute the angle of reflection using the digital surface model (DSM) derived from the LiDAR data. Then, a comparison between the results of the intensity data after RC using the scan angle and RC using the angle of reflection is carried out. The comparison is done by converting the intensity data into equivalent image data and evaluating the classification results of the intensity image data. Preliminary findings show that: 1) the variance-to-mean ratio of the land cover features are significantly reduced while using the angle of reflection in the RC process; 2) 4% of accuracy improvement can be achieved using the intensity data corrected with the scan angle. The accuracy improvement increases to 8% when using the intensity data corrected with the angle of reflection. The research work practically justifies the use of the reflection angle in the RC process of airborne LiDAR intensity data.