Terrestrial laser scanning sensors deliver not only three-dimensional geometric information of the scanned objects but also the intensity data of returned laser pulse. Recent studies have demonstrated potential applications of intensity data from Terrestrial Laser Scanning (TLS). However, the distance and incident angle effects distort the TLS raw intensity data. To overcome the distortions, a new intensity correction method by combining the piecewise fitting and overlap-driven adjustment approaches was proposed in this study. The distance effect is eliminated by the piecewise fitting approach. The incident angle effect is eliminated by overlap-driven adjustment using the Oren–Nayar model that employs the surface roughness parameter of the scanned object. The surface roughness parameter at a certain point in an overlapped region of the multi-station scans is estimated by using the raw intensity data from two different stations at the point rather than estimated by averaging the surface roughness at other positions for each kind of object, which eliminates the estimation deviation. Experimental results obtained by using a TLS sensor (Riegl VZ-400i) demonstrate that the proposed method is valid and the deviations of the retrieved reflectance values from those measured by a spectrometer are all less than 3%.

In precision farming, the separation of crops from soil is crucial for monitoring growth and fertilization. In this letter, a novel method is proposed for the accurate detection of corn seedlings from cropland by combining terrestrial laser scanning (TLS) and camera data. First, a piecewise linear interpolation method was used to eliminate the effect of distance on the TLS intensity data for more accurate intensity features of scanned targets. Second, the point cloud and camera data were registered to obtain the true color of each point in the point cloud. Third, we used a random forest algorithm to separate corn seedlings from soil by combining the geometric features from the TLS data with the radiometric features including the corrected intensity and RGB values derived from the TLS and camera data. To evaluate the proposed method, a case study was conducted by using a commercial TLS sensor with an embedded camera. The results demonstrated that corn seedlings can be separated from soil with an accuracy of 98.8% by using both the geometric and radiometric features, which is significantly higher than that by using any one of the two kinds of features.