Bayesian Model for 3D Undulating Terrain Depth Estimation Using Photon Counting LiDAR

Author:

Wang Rui1234,Liu Bo1234ORCID,Li Zhikang1234ORCID,Yi Hao1234,Guo Zeyu1234,Chen Zhen123

Affiliation:

1. National Key Laboratory of Optical Field Manipulation Science and Technology, Chinese Academy of Sciences, Chengdu 610209, China

2. Key Laboratory of Science and Technology on Space Optoelectronic Precision Measurement, Chinese Academy of Sciences, Chengdu 610209, China

3. Institute of Optics and Electronics, Chinese Academy of Sciences, Chengdu 610209, China

4. University of Chinese Academy of Sciences, Beijing 100049, China

Abstract

Photon counting LiDAR can capture the 3D information of long-distance targets and has the advantages of high sensitivity and high resolution. However, the noise counts restrict improvements in the photon counting imaging quality. Therefore, how to make full use of the limited signal counts under noise interference to achieve efficient 3D imaging is one of the main problems in current research. To address this problem, in this paper, we proposes a 3D imaging method for undulating terrain depth estimation that combines constant false alarm probability detection with the Bayesian model. First, the new 3D cube data are constructed by adaptive threshold segmentation of the reconstructed histogram. Secondly, the signal photons are extracted in the Bayesian model, and depth estimation is realized from coarse to fine by the sliding-window method. The robustness of the method under intense noise is proven by sufficient undulating terrain simulations and outdoor imaging experiments. These results show that the proposed method is superior to typical existing methods.

Publisher

MDPI AG

Subject

General Earth and Planetary Sciences

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