Objective  With rich data and wide scanning range, three-dimensional lidar is widely used in obstacle detection, terrain reconstruction, target detection, classification and tracking, as well as forestry and agricultural remote sensing. When using point cloud data for object description, people usually describe the details of objects through point cloud interpolation, and distinguish the types of objects through point cloud classification identifiers. The above functions can be achieved with high spectral full waveform lidar through waveform decomposition and spectral reconstruction. However, when there are multiple targets in the laser beam forming a shielding relationship, due to the close spacing and light spot splitting, it is difficult to accurately obtain target time-spectral information to reverse the target position and reflectivity distribution. For this purpose, we design a hyperspectral waveform decomposition method and corresponding point cloud expansion and identification method.

  Methods  Considering the spatial correlation and shape characteristics of the echo waveforms of various spectral channels, a new hyperspectral full waveform lidar waveform data processing method is proposed (Fig.1). Based on the prior knowledge obtained from multi-channel echo waveform comparison, inter channel correction is used to further improve the extraction accuracy of multi-target waveform components. Based on this new waveform decomposition method, the time-spectral information of the target can be accurately obtained. Accordingly, a point cloud extension and point cloud identification methods based on principal component analysis and random forest algorithm are proposed (Fig.2).

  Results and Discussions   With a full-waveform hyperspectral lidar, camouflage nets, and two diffuse reflective plates with known reflectivity, point cloud expansion and identification verification experiments were conducted. The experiment broke through the range resolution limited by pulse width under dense shielding conditions, resulting in a triple expansion of point cloud data on the target board. The target reflectance spectrum recovered by the proposed algorithm has a high similarity to the actual reflectance spectrum of the target, and the point cloud identification result perfectly distinguishes two target plates at the same distance.

  Conclusions  A new hyperspectral waveform decomposition scheme is proposed to solve the problems encountered in the generation of three-dimensional point clouds for hyperspectral full waveform lidar during the detection of obscured targets, such as the difficulty in resolving close range targets, and the inaccurate acquisition of spectral information caused by light spot splitting. Based on this, a method for expanding and identifying point cloud data is proposed. Experimental results show that even under dense camouflage nets, accurate waveform decomposition and spectral reconstruction can be achieved with the proposed waveform processing method, thereby achieving point cloud expansion and target type identification. When the distance between the shield and the target is smaller than the resolution distance determined by the laser pulse width, the method still has good point cloud expansion ability.