## 2 Materials and Methods
### Data Extraction
#### 2.3.2 Image Data Extraction  
The data extraction stage is responsible for extracting monthly values for the analysed bands and vegetation indices from the multispectral images, for each FB or VEG areas. The outputs from this procedure are a time-series (used for the feature selection and visualization of data) and a dataset for the detection and algorithm training. To achieve these goals, the following steps are performed:  
1. Extraction of the pixel values from the FB for each band in analysis and calculation of the mean value for the object representation;
2. Normalization of the band values;
3. Generation of monthly values for the FB or VEG regions;
4. Calculation of the defined spectral indices;
5. Concatenation of the previous month values to each month, to include temporal information. \begin{table}
\begin{tabular}{c c} \hline \hline
**Test** & **NRMSE** \\ \hline \(T1\) & 9\% \\ \(T2\) & 4\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of the Geolocation Correction. Figure 9: Geolocation correction in Band 04 images from Seia: (**a**) reference image (18-08-2017); (**b**) corrected image with an estimated offset of 0.7 North/South and \(-0.11\) East/West (24-07-2017). As the goal is to detect the maintenance operation in an object, step 1 is responsible for their representation by a value. The chosen metric was the mean value. To validate it is a representative measure to describe an FB, the relative standard deviation was calculated [28] and 0.185 was obtained. Since it is much smaller than 1, it is a reasonable approximation [29]. In step 2, the band values are normalized with the min-max technique. The minimum and maximum values corresponding to the radiometric resolution of the bands are [0;4095] and the range of normalization [0;1]. A requirement for the change detection classifier is the definition of a timestep. Additionally, time-series are noisy signals and to deal with this, a mean and a high median filter (when the set have a pair number of elements, instead of being the mean of the median elements, it is the higher element) were applied separately, and a comparison was made. A monthly timestep was defined, because two months without observations is unusual in the chosen study areas. In the case that there is not any clear observation for a month, the previous month's values are used. Figure 10a demonstrates that mean and median filters achieve the intended noise reduction when applied with a monthly window. Additionally, it is shown that the mean filter smooths the data more than the median filter. It is thus expected that the mean filter decreases the ability for the detection of operations. However, the median filter is more sensible to the noise, as shown in Figure 10b, and can origin more false positives, i.e., the detection of incorrect maintenance operations. In Figure 10b, an example of the performance of the noise reduction filters in an FB in Fundao is shown. It can be observed in the bars plot that when an operation occurs (August), the change in data is greater with a median filter. Although, if the operation occurs in the last observation of the month, the median value will be a value one before the cut, hiding the change being detected in the next month. Step 4 begins with the calculation of the spectral indices using the monthly values calculated in the previous step. Alternatively, these could be obtained by calculating the spectral indices per pixel and then aggregating them in a monthly value. However, the first approach saves processing time and does not represent a loss of information that compromises the problem solving. This is proved by Figure 11, where the results for the NDVI using the pixel and object computation with a median filter are presented. The NRMSE between the data obtained from these techniques is 7.4%, and if a mean filter is used, this value is 1.4%, which demonstrates the similarity between the data acquired. The maintenance detections rely on the temporal disturbances of the data.