3D target detection and spectral classification for single-photon LiDAR data.
3D target detection and spectral classification for single-photon LiDAR data.
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DOI：
10.1364/oe.487896
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发表时间：
2023-02
期刊：
影响因子：
3.8
通讯作者：
Mohamed Amir Alaa Belmekki;Jonathan Leach;Rachael Tobin;G. Buller;S. Mclaughlin;Abderrahim Halimi
中科院分区：
文献类型：
--
作者：
Mohamed Amir Alaa Belmekki;Jonathan Leach;Rachael Tobin;G. Buller;S. Mclaughlin;Abderrahim Halimi
三维单光子激光雷达成像在许多应用中具有重要作用。然而，全面部署这种模式将需要分析低信噪比的目标回报和非常大的数据量。这在通过遮挡物或在高环境背景光条件下成像时尤为明显。本文提出了一种基于光子时间直方图的三维表面检测的多尺度方法，可以显著减少数据量。得到的表面是无背景的，可以用来推断目标的深度和反射率信息。我们通过提出用于多光谱单光子激光雷达数据的三维重建和光谱分类的分层贝叶斯模型来证明这一点。重构方法提高了点云估计之间的空间相关性，并采用坐标梯度下降算法进行参数估计。仿真和真实数据的结果表明，与最先进的处理算法相比，所提出的目标检测和重建方法具有优势。
3D single-photon LiDAR imaging has an important role in many applications. However, full deployment of this modality will require the analysis of low signal to noise ratio target returns and very high volume of data. This is particularly evident when imaging through obscurants or in high ambient background light conditions. This paper proposes a multiscale approach for 3D surface detection from the photon timing histogram to permit a significant reduction in data volume. The resulting surfaces are background-free and can be used to infer depth and reflectivity information about the target. We demonstrate this by proposing a hierarchical Bayesian model for 3D reconstruction and spectral classification of multispectral single-photon LiDAR data. The reconstruction method promotes spatial correlation between point-cloud estimates and uses a coordinate gradient descent algorithm for parameter estimation. Results on simulated and real data show the benefits of the proposed target detection and reconstruction approaches when compared to state-of-the-art processing algorithms.