## The Set of Cues Evaluated in This Study  
The following cues which are derived from the individual channels are evaluated: (i) Range: cornerness, sum of derivatives, and the sum of the absolute values of the derivatives. (ii) Intensity: the intensity values itself and cornerness. (iii) Color: HSV and Lux representation. Their mathematical formulation is:  
**Range**:  
\((i)+(ii)\) _Sum & Absolute Sum of the First-Order Derivatives_  
\[f_{1}=\sum\left(d_{\phi}+d_{\theta}\right) \tag{10}\]  
\[f_{2}=\sum\left(\left|d_{\theta}\right|+\left|d_{\theta}\right|\right) \tag{11}\]  
with \(d_{\varphi}\) and \(d_{\theta}\) the first-order derivatives of the polar image in the directions of its two axes. Since all three features involve summation, they are area dependent, and therefore are normalized with respect to the segment area. The choice of the two features is motivated by [PERSON] _et al_. [30] who test the descriptiveness of the derivatives for the SURF descriptor. These two features measure texture characteristics within the segmented area. Since trees have high range variability in all directions, the first feature should have low values (positive and the negative values cancel one another), while the second feature yields high values. \((iii)\) _Cornerness of the Segment_  
\[f_{3}\left(L_{\tau}\right)=\sum\textit{cornerness}\left(L_{\tau}\right) \tag{12}\]  
is motivated by the cluttered structure of the trees, which is expected to generate distinctive corner values due to the lack of structure. Cornerness is computed using the min-max operator [31]. The gradient projection in the minimal direction measures the point \"cornerness\" (_Cn_). Generally, because of their complex shape and depth variability, tree related segments tend to have high cornerness values. In computing gradients, the need to control varying object-to-background distances arises. The potential mixture between object and background may arise from the 2D representation of the 3D data, and may lead to very steep gradients when the background is distant. To handle this, we erode the border pixels and do not sum their derivative value, thereby keeping the texture measures to \"within\" the segment only. Additionally, we trim the magnitude of possible derivatives by a threshold to eliminate background effects, so that backgrounds that are closer and farther from the object (which is irrelevant for the classification task) will have the same contribution to the derivatives related features. **Intensity**:  
(_i_) _Intensity Value_  
The choice of the intensity value as an attribute was motivated by the distinctive signature of vegetation in the near-infra-red spectral range, in which the scanner operates. The intensity value is expected to exhibit high values for vegetation features  
(_ii_) _Cornerness_  
The motivation is similar to that of using cornerness in the range data. Cornerness is computed using the min-max operator. **Color**:  
Two alternative color space representations were evaluated, the HSV and Luv. The first has an immediate link to the RGB representation, while the second is tightly linked to the XYy representation, which provides an undistorted color chart [32]. (_i_) HSV _channels_  
The Hue, Saturation, Value model [33] captures the color content of the image objects. Trees are expected to be characterized by unique hue and saturation values relative to non-tree objects. The HSV are computed from the RGB values via:  
\[\alpha=\frac{1}{2}\big{(}2R-G-B\big{)} \tag{13}\]  
\[\beta=\frac{\sqrt{3}}{2}\big{(}G-B\big{)}a \tag{14}\]  
\[H=\text{atan2}(\beta,\alpha) \tag{15}\]  
\[S=\sqrt{a^{2}+b^{2}} \tag{16}\]  
(_ii_) Luv _Channels_  
CIE-Luv is an attempt to define an encoding with uniformity in the perceptibility of color differences [32]. The non-linear relations for \(L\), \(u\), and v are given by:  
\[\textit{XYZ}= \begin{bmatrix}0.4125&0.3576&0.1804\\ 0.2125&0.7154&0.0721\\ 0.0193&0.1192&0.9502\end{bmatrix}