Integration of 2D and 3D features from UAV imagery for informal settlement classification using Multiple Kernel Learning

Informal settlement upgrading projects require high-resolution and up-to-date thematic maps in order to plan and design effective interventions. To this end, Unmanned Aerial Vehicles (UAVs) provide the opportunity to obtain very high resolution 2D orthomosaics and 3D point clouds where and when needed. The heterogeneous, dense structures which typically make up an informal settlement motivate the importance of integrating complex 2D and 3D features obtained from UAV data into a single classification problem. Multiple Kernel Learning (MKL) Support Vector Machines (SVMs) maintain the distinct characteristics of the different feature spaces by optimizing individual kernels for specific feature groups which are later combined into a single kernel used for classification. Both the kernel parameters and kernel weights can be optimized by considering the alignment between the kernel and an ideal kernel which would perfectly classify the samples. This paper demonstrates how extracting high-level features from both the 2D orthomosaic as well as the 3D point cloud (obtained by an UAV), and integrating them through a MKL approach, can obtain an Overall Accuracy of 90.29%, a 4% increase over the results obtained using single kernel methods.