Fast Task-Based Adaptive Sampling for 3D Single-Photon Multispectral Lidar Data
Fast Task-Based Adaptive Sampling for 3D Single-Photon Multispectral Lidar Data
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DOI：
10.1109/tci.2022.3150974
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发表时间：
2021-09
影响因子：
5.4
通讯作者：
Mohamed Amir Alaa Belmekki;Rachael Tobin;G. Buller;S. Mclaughlin;Abderrahim Halimi
中科院分区：
文献类型：
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作者：
Mohamed Amir Alaa Belmekki;Rachael Tobin;G. Buller;S. Mclaughlin;Abderrahim Halimi
3D单光子LiDAR成像在许多应用中发挥着重要作用。然而，长的采集时间和大量的数据量对LiDAR成像提出了挑战。本文提出了一种任务优化的自适应采样框架，使高维单光子激光雷达数据的快速采集和处理。给定一个感兴趣的任务，迭代采样策略的目标是最丰富的区域的场景被定义为那些最小化参数的不确定性。该任务是通过考虑一个贝叶斯模型，精心构建，以允许快速的每像素计算，同时提供量化的不确定性参数估计。该框架被证明在多光谱三维单光子激光雷达成像时，考虑对象分类和/或目标检测的任务。它还分析了顺序和并行扫描模式，不同的探测器阵列的大小。模拟和真实的数据的结果表明，所提出的优化的采样策略相比，国家的最先进的采样策略的好处。
3D single-photon LiDAR imaging plays an important role in numerous applications. However, long acquisition times and significant data volumes present a challenge for LiDAR imaging. This paper proposes a task-optimized adaptive sampling framework that enables fast acquisition and processing of high-dimensional single-photon LiDAR data. Given a task of interest, the iterative sampling strategy targets the most informative regions of a scene which are defined as those minimizing parameter uncertainties. The task is performed by considering a Bayesian model that is carefully built to allow fast per-pixel computations while delivering parameter estimates with quantified uncertainties. The framework is demonstrated on multispectral 3D single-photon LiDAR imaging when considering object classification and/or target detection as tasks. It is also analysed for both sequential and parallel scanning modes for different detector array sizes. Results on simulated and real data show the benefit of the proposed optimized sampling strategy when compared to state-of-the-art sampling strategies.