Radiometric calibration of a dual-wavelength terrestrial laser scanner using neural networks

Abstract

ABSTRACTThe Salford Advanced Laser Canopy Analyser (SALCA) is a unique dual-wavelength full-waveform terrestrial laser scanner (TLS) designed to measure forest canopies. This article has two principle objectives, first to present the detailed analysis of the radiometric properties of the SALCA instrument, and second, to propose a novel method to calibrate the recorded intensity to apparent reflectance using a neural network approach. The results demonstrate the complexity of the radiometric response to range, reflectance, and laser temperature and show that neural networks can accurately estimate apparent reflectance for both wavelengths (a root mean square error (RMSE) of 0.072 and 0.069 for the 1063 and 1545 nm wavelengths, respectively). The trained network can then be used to calibrate full hemispherical scans in a forest environment, providing new opportunities for quantitative data analysis.