# Burned Area Classification Based on Extreme Learning Machine and Sentinel-2 Images
## 3 Materials and Methods
### Experiments  
The original set was randomly divided into three sets with the following percentages of the total amount of data: 50% for the training set, 20% for the validation set and 30% for the test set. The training set is used to calculate the classifiers parameters, the validation set offers the chance to choose the best model among those generated by the combination of hyperparameters and the test set is used to carry out the objective performance evaluation of each classification algorithm. The following variations to the hyperparameters of the classification algorithms were conducted:  
* ELM: the best model was selected among ELM networks with 1, 2, 3, 4,....., 498, 499 and 500 neurons in the hidden layer.
* Random forest: training and tests were conducted with 5, 10, 15, 20,..., 985, 990, 995 and 1000 random trees.
* Logistic regression: training and tests were conducted with 5, 10, 15, 20,..., 985, 990, 995 and 1000 as the maximum number of iterations for convergence.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Index** & **Formulation** \\ \hline BAI [41] & \(\dfrac{1}{(0.1-b4)^{2}+(0.06-b8A)^{2}}\) \\ BAIS2 [5] & \(\left(1-\sqrt{\frac{b6\cdot b7\cdot 88A}{b4}}\right)\cdot\left(\frac{b12-b8A}{ \sqrt{b12+b8A}}+1\right)\) \\ MIRBI [42] & \(\left(10\cdot b12-9.8\cdot b11+2\right)\) \\ NBR2 [43] & \(\dfrac{(b11-b12)}{(b11+b12)}\) \\ \hline \hline \end{tabular} where: \(b4\): Reflectance in the red band (centered at 0.665 μm). \(b6\): Reflectance in the near-infrared band 1 (centered at 0.665 μm). \(b8A\): Reflectance in the near-infrared band 2 (centered at 0.865 μm). \(b11\):Reflectance in the shortwave infrared band 1 (centered at 1.610 μm). \(b12\): Reflectance in the shortwave infrared band 1 (centered at 2.190 μm).  
\end{table}
Table 2: Spectral indices used for burned area identification.  
Support vector machine: a polynomial core was used that shows a better performance than radial basis kernel [48] and that has been widely applied in this type of classification [49]. Multilayer perceptron: training and tests were carried out with a single hidden layer multilayer perceptron, from 1 to 100 neurons, with rectified linear unit activation function (relu) and using the Adam stochastic optimizer [50].  
In order to choose the best model among the variations generated by the hyperparameters, the average of global accuracy and the DICE coefficients [9] was calculated over the validation set (Figure 3).  
According to [PERSON] et al. [51], obtaining an accurate analysis of burned classifications or products is much more relevant to consider the accuracy of the \"burned\" category than the \"unburned\" because the first is more related to the impacts of biomass burning and its atmospheric effects. For that, the performance of the best models for the burned category was evaluated in the test set with DICE coefficient, omission and commission errors, which have been used in several studies that compared accuracies for burned area classifications [9; 13; 52]. The DICE coefficient measures accuracy with a probabilistic meaning [40], and the accuracy, omission and commission errors are getting from the confusion matrix [53]. In addition, we evaluated the models according to their training time [14]. Finally, the performance of the selected model was compared using an independent validation area. The validation area was built from a supervised classification of Sentinel-2 (20 m) images from a section of 247,401 hectares from the Maule Region, representing 2.96% of the study area. The classification performance of the best model was compared to the estimations of burned area done by the Chile's National Forest Corporation (CONAF) ([[https://sit.conaf.cl/](https://sit.conaf.cl/)]([https://sit.conaf.cl/](https://sit.conaf.cl/)), accessed on 23 September 2021) and the FireCCI product [54].