A Classification Method for Airborne Full-Waveform LiDAR Systems Based on a Gramian Angular Field and Convolution Neural Networks

Abstract

The data processing of airborne full-waveform light detection and ranging (LiDAR) systems has become a research hotspot in the LiDAR field in recent years. However, the accuracy and reliability of full-waveform classification remain a challenge. The manual features and deep learning techniques in the existing methods cannot fully utilize the temporal features and spatial information in the full waveform. On the premise of preserving temporal dependencies, we convert them into Gramian angular summation field (GASF) images using the polar coordinate method. By introducing spatial attention modules into the neural network, we emphasize the importance of the location of texture information in GASF images. Finally, we use open source and simulated data to evaluate the impact of using different network architectures and transformation methods. Compared with the performance of the state-of-art method, our proposed method can achieve higher precision and F1 scores. The results suggest that transforming the full waveform into GASF images and introducing a spatial attention module outperformed other classification methods.

Coupling Dilated Encoder–Decoder Network for Multi-Channel Airborne LiDAR Bathymetry Full-Waveform Denoising

“…However, hand-extracted features require specialized knowledge and extensive preprocessing. The feature weights of key positions can be optimized by converting the airborne waveform into a time-series diagram and extracting the high-dimensional temporal and spatial features of the Gramian angular field time-series diagram using the mature network architecture in the image field [35,36]. Based on the LSTM module and one-dimensional convolution module, the time sequence dependence and local spatial features of signals can be extracted [37,38].…”