A maximum entropy-based optimal neighbor selection for multispectral airborne LiDAR point cloud classification

Abstract

Multispectral light detection and ranging (LiDAR) technology was recently invented to improve the capability of thematic mapping through incorporating visible/infrared spectral information. Similar to image processing, point cloud classification usually considers contextual features derived from surrounding points to improve the model accuracy. Some of the existing methods construct contextual features of point clouds by querying a fixed scale/number of neighbor points or selecting a variable size neighborhood based on some optimality criterion. Although these methods are able to collect neighbor points to derive contextual features, they may also in turn introduce heterogeneity from the local neighborhood or select insufficient neighbor points, hindering the performance of classification. Therefore, we propose an optimal neighbor selection method based on the maximum entropy (MaxEnt) principle. More specifically, the proposed method determines the homogeneity of local neighborhood of each point and constructs geometric and radiometric features based on the use of MaxEnt to determine optimal points nearby. The constructed contextual features are then served as input into various machine learning classifiers for point cloud classification. Extensive experiments are conducted to compare the performance of MaxEnt against six other neighbor selection methods. The experimental results demonstrate that MaxEnt is able to achieve better classification results on multispectral airborne LiDAR data collected by Optech Titan in terms of overall accuracy (OA) improvement by 7.3%–19.1%. Moreover, MaxEnt is proven to be more suitable for land cover scenarios with imbalanced classes caused by detailed and tiny objects, e.g., perimeter fencings and power lines, than other existing neighbor selection methods.