# Burned Area Classification Based on Extreme Learning Machine and Sentinel-2 Images
###### Abstract  
Sentinel-2 satellite images allow high separability for mapping burned and unburned areas. This problem has been extensively addressed using machine-learning algorithms. However, these need a suitable dataset and entail considerable training time. Recently, extreme learning machines (ELM) have presented high precision in classification and regression problems but with low computational cost. This paper proposes evaluating ELM to map burned areas and compare them with other machine-learning algorithms broadly used. Several indices, metrics and training times were used to assess the performance of the algorithms. Considering the average of datasets, the best performance was obtained by random forest (DICE = 0.93; omission and commission = 0.08) and ELM (DICE = 0.90; omission and commission = 0.07). The training time for the best model was from ELM (1.45 s) and logistic regression (1.85 s). According to results, ELM was the best burned-area classification algorithm, considering precision and training time, evidencing great potential to map burned areas at global scales with medium-high spatial resolution images. This information is essential to fire-risk systems and burned-area records used to design prevention and fire-combat strategies, and it provides valuable knowledge on the effect of fires on the landscape and atmosphere. remote sensing: burned area classification; extreme learning machine; sentinel-2 images +
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In general terms, there are two approaches for the mapping of burned areas using satellite data. The first one is based on rules that identify variations in the burned spectral signals with respect to the unburned environment, based on which fixed or dynamic thresholds are defined for the spectral bands or indices [6]. The second approach is based on machine-learning algorithms that can learn the spectral features of a sample of pixels with labels and recognize those patterns in other areas of the image [7]. Machine-learning (ML) algorithms build a non-linear transformation whose parameters are estimated in a supervised or unsupervised way to solve classification, regression and clustering problems by formulating an optimization problem [8]. Recently, these types of algorithms have been used for mapping burned areas, showing good results with random forest (RF) [7; 9], support vector machine (SVM) [7; 10], logistic regression (LR) [11] and multilayer perceptron (MLP) supervised learning Neural Networks [7; 12]. Despite the advances in the methodologies and algorithms employed, the estimation of burned areas using satellite images depends heavily on the spatial resolution of the sensor used. This demonstrates the significant underestimation of the calculations performed with low-resolution sensors when compared to estimations of higher resolution [13]. Therefore, it is deemed necessary to explore the use of higher spatial resolution platforms and sensors, such as Sentinel-2, in order to deal with these problems. Moreover, most of the machine-learning algorithms that are frequently employed in image classification increase their computational cost when the size of the data sets increases, leading to longer training and classification times [14]. However, extreme learning machines (ELM) have recently attracted increasing interest in image classification due to their high accuracy levels, which are comparable to the traditional SVM and MLP, but with a training algorithm with reduced computational cost [15]. Considering the ELM algorithm training efficiency and its good generalization, we hypothesized that ELM neural networks can be used to classify burned areas on medium-spatial resolution images, showing a great potential to deal with this problem at scales that require a massive volume of data. Consequently, this work aims to propose the evaluation of mapping burned areas on Sentinel-2 images through an ELM neural network supervised classification, comparing ELM performance in terms of accuracy and training time to several machine-learning algorithms.