Abstract
Nowadays, unmanned aerial vehicles (UAVs) are viewed as effective acquisition platforms for several civilian applications. They can acquire images with an extremely high level of spatial detail compared to standard remote sensing platforms. However, these images are highly affected by illumination, rotation, and scale changes, which further increases the complexity of analysis compared to those obtained using standard remote sensing platforms. In this paper, we introduce a novel convolutional support vector machine (CSVM) network for the analysis of this type of imagery. Basically, the CSVM network is based on several alternating convolutional and reduction layers ended by a linear SVM classification layer. The convolutional layers in CSVM rely on a set of linear SVMs as filter banks for feature map generation. During the learning phase, the weights of the SVM filters are computed through a forward supervised learning strategy unlike the backpropagation algorithm widely used in standard convolutional neural networks (CNNs). This makes the proposed CSVM particularly suitable for detecting problems characterized by very limited training sample availability. The experiments carried out on two UAV data sets related to vehicles and solar-panel detection issues, with a 2-cm resolution, confirm the promising capability of the proposed CSVM network compared to recent state-of-the-art solutions based on pretrained CNNs.