End-to-End Full-Waveform Echo Decomposition Based on Self-Attention Classification and U-Net Decomposition

Abstract

Different from conventional decomposition methods, which utilize several steps to obtain the final result, a self-attention-based neural network, Attention Full-waveform Decomposition Network (AFD-Net), is discussed in this article for end-to-end full-waveform LiDAR signal decomposition. In existing LiDAR waveform decomposition methods, complicated functional models are used to fit echo components. Thus, the echo decomposition problem can be translated into a function approximation task. Recent studies present great progress in estimating the parameters of fitting models, hence, in the final decomposition results. However, the shape of received echo components is always irregular. None of the parametric functional models can fit the received echo components perfectly, which leads to unavoidable errors in the initial step of echo decomposition. In this article, we propose an end-to-end network AFD-Net to solve the echo decomposition problem without assuming any parametric functional models. AFD-Net consists of two modules: 1) the classification module and 2) the decomposition module. The former module is used to determine the number of echo components in a received waveform. Then, the decomposition module is used to output the echo components. By experiments, we have a classification accuracy of 96$\%$ using the first module. The average $R^{2}$ coefficient for the decomposed echo components is 0.9799. In addition, there are no public datasets for the waveform decomposition task available. Thus, another contribution of our work is to develop a tool to generate synthetic full-waveform LiDAR signals, which can help researchers to construct their own dataset for related works.

Keywords

• End-to-end neural network
• full-waveform data- sets
• full-waveform LiDAR
• self-attention
• waveform decompo- sition