Detection of urban features by multilevel classification of multispectral airborne LiDAR data fused with very high-resolution images

Abstract

A complex pattern of urban demographic transition has been taking shape since the onset of the COVID-19 pandemic. The long-standing rural-to-urban route of population migration that has propelled waves of massive urbanization over the decades is increasingly being juxtaposed with a reverse movement, as the pandemic drives urban dwellers to suburban communities. The changing dynamics of the flow of residents to and from urban areas underscore the necessity of comprehensive urban land-use mapping for urban planning/management/assessment. These maps are essential for anticipating the rapidly evolving demands of the urban populace and mitigating the environmental and social consequences of uncontrolled urban expansion. The integration of light detection and ranging (LiDAR) and imagery data provides an opportunity for urban planning projects to take advantage of its complementary geometric and radiometric characteristics, respectively, with a potential increase in urban mapping accuracies. We enhance the color-based segmentation algorithm for object-based classification of multispectral LiDAR point clouds fused with very high-resolution imagery data acquired for a residential urban study area. We propose a multilevel classification using multilayer perceptron neural networks through vectors of geometric and spectral features structured in different classification scenarios. After an investigation of all classification scenarios, the proposed method achieves an overall mapping accuracy exceeding 98%, combining the original and calculated feature vectors and their output space projected by principal components analysis. This combination also eliminates some misclassifications among classes. We used splits of training, validation, and testing subsets and the k-fold cross-validation to quantitatively assess the classification scenarios. The proposed work improves the color-based segmentation algorithm to fit object-based classification applications and examines multiple classification scenarios. The presented scenarios prove superiority in developing urban mapping accuracies. The various feature spaces suggest the best urban mapping applications based on the available characteristics of the obtained data.