Comparative analysis of classification techniques for building block extraction using aerial imagery and LiDAR data

Abstract

Building detection has been a prominent area in the area of image classification. Most of the research effort is adapted to the specific application requirements and available datasets. In this paper we present a comparative analysis of different classification techniques for building block extraction. Our dataset includes aerial orthophotos (with spatial resolution 20cm), a DSM generated from LiDAR (with spatial resolution 1m and elevation resolution 20 cm) and DTM (spatial resolution 2m) from an area of Athens, Greece. The classification methods tested are unsupervised (K-Means, Mean Shift), and supervised (Feed Forward Neural Net, Radial-Basis Functions, Support Vector Machines). We evaluated the performance of each method using a subset of the test area. We present the classified images, and statistical measures (confusion matrix, kappa coefficient and overall accuracy). Our results demonstrate that the top unsupervised method is the Mean Shift that performs similarly to the best supervised methods.