# Object-Based Land Cover Classification of Cork Oak Woodlands using UAV Imagery and Orfeo ToolBox
## 5 Discussion  
### Segmentation  
Given that it was expected to worsen the results, the smoothing step of the standard segmentation workflow provided in OTB was omitted because it reduces the contrast necessary to discriminate between the cork oak and shrub classes. The optimization of the range radius and minsize values led to a consistent separation between cork oak, shrubs, and grass classes.  
To accomplish the segmentation step with the high structural and compositional heterogeneity of the site, we adopted a low-scale factor of objects, and this was effective. Actually, in the LSMS-segmentation algorithm, the scale factor can be only managed through the adopted values of the range radius and minimum region size parameters. As reported by scholars [56; 57; 58; 59], even today, the visual interpretation of segmentation remains the recommended method to assess the quality of the obtained results.  
Taking into account the high geometrical resolution of our imagery (i.e., GSD = 10 cm), a low scale of segmentation was preferable; thus, relatively low values of the range radius and minimum region size were used. Even though producing very small segments, in some cases, leads to over-segmentation (i.e., a single semantic feature is split into several segments, which then need to be merged), a low scale of objects effectively resulted in a high degree of accuracy. It allowed the identification of polygons that represent every small radiometric difference in tree crowns, as well as small patches of grass and shrubs (Figure 3). As highlighted in several studies [29; 60; 61; 62], a certain degree of over-segmentation is preferable to under-segmentation to improve the classification accuracy, as can be clearly inferred from the results in Tables 3 and 4.  
It is important to underline a very significant difference in the number of polygons in the segmentation results of the two different mosaics, despite our application of region merging using the same threshold size (250 pixels). As shown in the results section, the summer image presents almost twice the number of polygons relative to the spring image. This might be explained by a higher chromatic difference caused by a high contrast of vegetation suffering from drought (especially _Cistus_ and the herbaceous layer) and by an increased presence of shadows (an increase of more than 4% of the study area compared with the spring image). Actually, the summer flight was performed earlier in the day (11:00) than the spring one (13:00), so the flights were subjected to different sun elevation angles (Table A1). Nevertheless, Figure 3 shows that the obtained segmentation correctly outlines the boundaries of the tree crowns, as well as the shrub crowns and the surrounding grass spots, discriminating one from the other. The segmentation results also clearly differentiate between the bare soil and the vegetation, even in the presence of small patches. As reported in recent studies that performed forest tree classification [18; 59], the additional information provided by the DSM and NDVI significantly improves the delimitation and discrimination of segments, which was true in our case as well. This is more relevant in conditions in which the three vegetation layers are spatially close, because discrimination based only on spectral information has proven very difficult to achieve in such environments. An additional detail to mention is that the algorithm was able to accurately recognize the shadows, an important \"disturbing factor\" for image processing, since they are usually hardly distinguished from other objects. This is the reason why shadows were specifically considered as a separate class category.