# Object-Based Land Cover Classification of Cork Oak Woodlands using UAV Imagery and Orfeo ToolBox
## 3 Methods
### Classification and Accuracy Assessment
#### 3.4.1 Choice and Selection of the Training Polygons  
Before the selection of the training polygons, it was important to carry out an on-field exploratory survey of the region under study. After this, for each of the five land cover classes, 150-350 polygons were selected from each of the two five-band orthomosaics. These training polygons were manually selected by on-screen interpretation while taking into consideration a good distribution among the five land cover classes and the different chromatic shades characterizing the study area. As explained by [PERSON] and [PERSON] [46], to obtain a statistically consistent representation of the analyzed image, the accuracy assessment requires that an adequate number of samples should be collected per each land cover class. To this end, we considered it appropriate to select a total number of trainer polygons that did not deviate from 2% of the total number of segments of the entire image (5.48% and 3.01% in terms of covered surface for spring and summer images, respectively). Regarding the distribution among land cover classes, the number of samples was defined according to the relative importance of that class within the classification objectives.  
.4.2 Implementation of Support Vector Machine (SVM) and Random Forest (RF) Classification Algorithms  
SVM is a supervised non-parametric classifier based on statistical learning theory [47; 48] and on the kernel method that has been introduced recently for the application of image classification [49]. SVM classifiers have demonstrated their efficacy in several remote sensing applications in forest ecosystems [49; 50; 51; 52].  
This classifier aims to build a linear separation rule between objects in a dimensional space (hyperplane) using a \(\varphi(\cdot)\) mapping function derived from training samples. When two classes are nonlinearly separable, the approach uses the kernel method to realize projections of the feature space on higher dimensionality spaces, in which the separation of the classes is linear [33; 34]. In their simplest form, SVMs are linear binary classifiers (-1/+1) that assign a certain test sample to a class using one of the two possible labels. Nonetheless, a multiclass classification is possible, and is automatically executed by the algorithm.  
Random forest is an automatic learning algorithm introduced by [PERSON] [53] and improved with the work of [PERSON] [54]. It consists of a set of decision trees and uses bootstrap aggregation, namely, bagging, in order to create different formation subsets for producing a variety of trees, any one of which provides a classification result. The algorithm classifies the input vector with every single decision tree and returns the classification label, which is the one with the most \"votes\". All of the decision trees are trained using the same parameters but on different training sets. The algorithm produces an internal and impartial estimation of the generalization error using so-called out-of-bag (OOB) samples, which contain observations that exist in the original data and do not recur in the bootstrap sample (i.e., the training subset) [54].  
The experiences of [PERSON] et al. [55] and [PERSON] et al. [41] show that the default values of the OTB parameters for training and classification processes seem to provide optimal results. In view of this, in this study we used default values for both classification algorithms. With SVM, we used a linear kernel-type with a model-type based on a C value equal to 1. For the RF algorithm, the maximum depth of the tree was set to 5, while the maximum number of trees in the forest was fixed to 100. The OOB error was set to 0.01.