Automatic Recognition of Illegal Substations by Employing Logit-Boost Algorithm and LSTM With the Help of Different Landsat-8 OLI Image Spectral Band Parameters: A Case Study in Sason, Turkey

Automatic recognition of illegal substations is of great importance, since most of the leakage electricity in Turkey is due to the use of these substations in agricultural fields. One of the most effective ways to detect illegal substations is to employ remote sensing images and machine learning techniques together. Because, thanks to remote sensing images, it is possible to analyze illegal substations on huge agricultural lands in a short time. In this study, illegal substations on the agricultural fields in the southeast Anatolian region, which is one of the regions where leakage electricity are most common, have been detected with the aid of Landsat-8 OLI images and machine learning algorithm. The proposed study has been carried out in several stages, respectively. In the first stage, the locations of 42 substations and 21 non-substation objects on the pilot area have been recorded with the help of GPS and these coordinates have been later transferred to the Landsat-8 OLI image dated on 14 June 2019. In the second stage, an image analysis has been performed by calculating the spectral band parameters from the Landsat-8 OLI images. In the next stage, relationships among illegal substations and non-substation objects have been set by utilizing the statistical metrics of obtained spectral band parameters. In the last stage, by utilizing LSTM (Long Short-Term Memory) method, which is a recurrent neural network model that has gained popularity in both remote sensing and various scientific disciplines in recent years and the Logit-Boost method, which is one of the popular boosting machine learning algorithms, automatic recognition of substations has been performed with an average accuracy of 88.89% for Logit-Boost method and 84.21% for LSTM method. It is notable from this study that the Logit-Boost Algorithm yields more proficient results than the LSTM model.