# Object-Based Land Cover Classification of Cork Oak Woodlands using UAV Imagery and Orfeo ToolBox
## 4 Results
### Segmentation  
The absence of suitable material in the literature from which to take a cue for the choice of parameter values drastically influenced the testing approach. The attainment of the optimal final values of the parameters of the LSMS-segmentation algorithm was effectively possible only by performing a long series of attempts using small sample areas according to theoretical fundamentals.  
Adopting spatial radius values lower than 10 led to results that were similar to those obtained without the smoothing step, while higher values in the smoothing phase worsened the obtained segmentation (Figure 4). Moreover, with values of the spatial radius that were lower than 10, the computation time of the smoothing process was around 10 hours, and it reached around thirty-six hours with a value of 30. Therefore, we decided to eliminate the smoothing step from our final workflow. In the second phase of our tests, we optimized the range radius and the minimum region size. We discarded range radius values lower than 6 because they led to over-segmentation, while values over 7 led to under-segmentation, which is characterized by poor segmentation for cork oak (Figure 4). For the minsize, we adopted a threshold obtained by measuring a number of smaller cork oak crowns (minimum values of about 2.5 m\({}^{2}\), corresponding to the adopted threshold of 250 pixels).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Spring**} \\ \hline  & **Red** & **Green** & **NIR** & **NDVI** & **DSM** \\
**M1,2** & 0.021 & **2.148** & 0.004 & 0.250 & 1.040 \\
**M1,3** & 0.473 & 0.700 & 0.387 & 0.777 & **1.199** \\
**M2,3** & 0.494 & **0.673** & 0.420 & 0.450 & 0.032 \\  & \multicolumn{4}{c}{**Summer**} \\ \hline  & **Red** & **Green** & **NIR** & **NDVI** & **DSM** \\
**M1,2** & 0.402 & 0.228 & 0.487 & **0.823** & 0.568 \\
**M1,3** & 0.139 & 0.308 & 0.303 & **2.000** & 0.739 \\
**M2,3** & 0.306 & 0.559 & 0.240 & **0.75** & 0.135 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Spectral separability (M-statistic) provided by red, green, NIR, NDVI, and DSM for both flights (spring and summer). Land cover classes are denoted as follows: 1 = cork oak; 2 = shrubs; 3 = grass. The highest value of the statistic is highlighted for each pair of classes.  
Figure 4: Visual comparison from some of the numerous tests carried out for the estimation of segmentation accuracy. On the left, a portion of the R-G-NIR image is shown; on the right, the same area is shown with the superimposed vector file containing the segmentation polygons (segments). The top images, from A1 to A3, show the segmentation results obtained without the smoothing step and a range radius of 15 (the default value), 5, and 7. The lower images, B1 and B2, show the segmentation results with the smoothing step and a spatial radius of 5 (the default value) and 30.  
The evaluation of different combinations of these parameters, and therefore of the obtained segmentations, was performed by means of visual interpretation by superimposing them over the orthomosaic. We obtained the most satisfactory results after setting the range radius value to 7 and the size of tiles to \(500\times 500\) pixels. These values proved to be the most reliable for both spring and summer imagery. The region merge step followed the segmentation stage, from which 26,815 and 49,575 segments were obtained for the spring and summer flights, respectively. Figure 4 shows a set of examples of the segmentation procedure with the superimposition of the vector file containing segments on the RGB image from a small portion of the study area.