Exploration of U-Net and Support Vector Machine classification methods for UAV multispectral image segmentation
Recently, many solutions have been introduced to accurately and automatically analyze data acquired with Unmanned Aerial Vehicles (UAVs), in particular by relying on algorithms based on Artificial Intelligence (AI) techniques. Among these, the most popular are those belonging to the category of neural networks. These techniques allow the development of ad-hoc and end-to-end solutions for the classification and segmentation of different object categories through the analysis of high-resolution multispectral images. In our research, two main methodologies have been explored for the automatic segmentation of crop rows from multispectral images acquired with UAVs. The first is based on Support Vector Machines, know to handle well overfitting issues, and the other through the implementation of “U-Net”, a state-of-the-art Convolution Neural Network.