Hyperspectral LiDAR is an emerging active remote-sensing technology that integrates the 3D spatial information acquisition capabilities of traditional LiDAR with the spectral information of hyperspectral imaging. It enables efficient, high-resolution, integrated spatial–spectral information acquisition and holds significant potential in fields like environmental awareness, as well as in forest and urban surveys.

Range resolution, a key metric in remote sensing, determines the ability to distinguish between two objects along a single line of sight. Traditional single-wavelength LiDAR is limited by the pulse width of the laser signal, resulting in limited range resolution. However, hyperspectral LiDAR can enhance the multi-targets’ range resolution through waveform processing and exploiting the correlation between multi-channel echoes without changing the pulse width.

This report first investigates the range-resolution enhancement mechanisms for hyperspectral LiDAR, focusing on waveform-processing techniques. Traditional methods, such as the multispectral waveform decomposition and multichannel interconnection decomposition method by Wuhan University, improve range resolution by comparing waveform decomposition methods or accumulate waveforms in different wavelengths to identify hidden waveform components. As a result, a 2 ns pulse width achieves 20 cm resolution, while a 4 ns pulse width reaches 43 cm resolution. However, these methods struggle with highly overlapped waveforms when target separation is extremely small.

To address this, we propose a novel solution: identifying highly overlapped waveforms before decomposition. When two targets are extremely close, the geometric center positions of overlapped waveforms recorded by different wavelengths exhibit aggregation and asymmetry, which is significantly different from the random distribution of single-target echoes. Then, we propose a new highly overlapped hyperspectral waveforms decomposition method. Our approach can enhance range resolution to 5 cm for a 4 ns pulse width. The report also discusses the potential of these technologies for complex target detection and future trends in hyperspectral LiDAR data processing, providing practical insights for related research.