A Comparative Analysis of Deep Learning-Based Segmentation Techniques for Terrain Classification in Aerial Imagery - Martina Formichini; Carlo Alberto Avizzano (AI)
Background: Deep convolutional neural networks (CNNs) have become widely popular for many imaging applications, and they have also been applied in various studies for monitoring and mapping areas of land. Nevertheless, most of these networks were designed to perform in different scenarios, such as autonomous driving and medical imaging. Methods: In this work, we focused on the usage of existing semantic networks applied to terrain segmentation. Even though several existing networks have been used to study land segmentation using transfer learning methodologies, a comparative analysis of how the underlying network architectures perform has not yet been conducted. Since this scenario is different from the one in which these networks were developed, featuring irregular shapes and an absence of models, not all of them can be correctly transferred to this domain. Results: Fifteen state-of-the-art neural networks were compared, and we found that, in addition to slight differences in performance, there were relevant differences in the numbers and types of outliers that were worth highlighting. Our results show that the best-performing models achieved a pixel-level class accuracy of 99.06%, with an F1-score of 72.94%, 71.5% Jaccard loss, and 88.43% recall. When investigating the outliers, we found that PSPNet, FCN, and ICNet were the most effective models. Conclusions: While most of this work was performed on an existing terrain dataset collected using aerial imagery, this approach remains valid for investigation of other datasets with more classes or richer geographical extensions. For example, a dataset composed of Copernicus images opens up new opportunities for large-scale terrain analysis.