Wavelet domain active learning for robust classification of full-waveform LiDAR data

Abstract

In this paper, we study a novel approach to active learning in the wavelet domain for classification of Full-waveform LiDAR (FWL) data. Unlike discrete 3-dimensional points obtained from a traditional discrete return LiDAR system, FWL systems have the capability to record the entire backscat-tered signal, which contains additional information about the reflecting objects. With such LiDAR systems, the vertical structure of reflectors is effectively characterized by the shape of the return pulse. Instead of deriving simple structure and statistics-based features, such as pulse amplitude and width, skewness, and kurtosis from the FWL data, in this work, wavelet features are extracted via a Redundant Discrete Wavelet Transform (RDWT) and are then utilized for classification in a multi-view active learning (AL) framework. Additionally, we demonstrate that the proposed approach provides a noise robust framework for analysis and classification of low Signal-to-Noise (SNR) LiDAR data. Experimental results demonstrate the efficacy of the proposed wavelet-based active learning for FWL data.