机载多输入三维激光雷达协同探测系统研究

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"这篇论文研究了机载多输入三维激光雷达协同探测系统,作者为李树锋和严光文,探讨了激光雷达在获取高精度地面三维信息中的应用和技术优势。该系统结合了激光技术、高动态载体姿态测定技术和动态差分GPS定位技术,与传统摄影测量方法相比,能更快、更准确地获取地表信息。" 正文: 机载多输入三维激光雷达(Light Detection and Ranging, LiDAR)协同探测系统是近年来发展起来的一种先进信息技术,它在地理空间数据获取和环境监测等领域具有重要应用。该系统主要由激光发射、接收、信号处理和载体姿态控制等部分组成,能够在高速移动的平台上对地表进行快速、高精度的三维扫描。 激光雷达技术的核心在于其非接触式的探测方式,通过发射激光脉冲并接收回波,计算目标的距离和速度。这种技术的优势在于其高分辨率和不受光照条件限制的特点,即使在夜晚或云雾天气下也能正常工作。机载LiDAR系统则进一步提升了这一优势,通过整合高动态载体姿态测定技术,可以实时、精确地获取飞行器的姿态信息,从而校正激光束的指向,确保数据的准确性。 动态差分GPS(Global Positioning System)定位技术是机载LiDAR系统的另一关键组成部分,它能够提供更高精度的位置信息,尤其是在城市峡谷和森林覆盖区域,可以显著提高点云数据的定位精度。与传统的摄影测量方法相比,机载LiDAR可以快速获取大规模的地形数据,而且不受地形遮挡的影响,对于复杂地形的测绘尤其有利。 协同探测的概念引入到机载LiDAR系统中,意味着多个传感器或数据源可以同时工作,互相补充,提升整体性能。例如,通过多个不同角度的激光雷达进行同步扫描,可以增强数据的完整性,减少盲区,提高三维重建的质量。此外,多输入系统还可以结合其他遥感数据,如光学影像和红外数据,实现多模态信息融合,为地表特征识别和环境分析提供更丰富的信息。 论文的作者李树锋和严光文在这一领域有深入的研究,他们所在的北京交通大学计算机与信息学院以及北京万兆达系统科技有限公司,都是我国在信息技术和遥感领域的研究机构。他们的研究不仅推动了机载LiDAR技术的发展,也为未来地球观测和环境保护提供了新的工具和思路。 机载多输入三维激光雷达协同探测系统是现代地理空间信息获取的重要手段,它的技术进步对地表特征的精细探测、地形测绘、灾害监测、城市规划等诸多领域都有着深远的影响。随着技术的不断成熟和成本的降低,这种系统在未来将有更广泛的应用前景。

Please revise the paper:Accurate determination of bathymetric data in the shallow water zone over time and space is of increasing significance for navigation safety, monitoring of sea-level uplift, coastal areas management, and marine transportation. Satellite-derived bathymetry (SDB) is widely accepted as an effective alternative to conventional acoustics measurements over coastal areas with high spatial and temporal resolution combined with extensive repetitive coverage. Numerous empirical SDB approaches in previous works are unsuitable for precision bathymetry mapping in various scenarios, owing to the assumption of homogeneous bottom over the whole region, as well as the limitations of constructing global mapping relationships between water depth and blue-green reflectance takes no account of various confounding factors of radiance attenuation such as turbidity. To address the assumption failure of uniform bottom conditions and imperfect consideration of influence factors on the performance of the SDB model, this work proposes a bottom-type adaptive-based SDB approach (BA-SDB) to obtain accurate depth estimation over different sediments. The bottom type can be adaptively segmented by clustering based on bottom reflectance. For each sediment category, a PSO-LightGBM algorithm for depth derivation considering multiple influencing factors is driven to adaptively select the optimal influence factors and model parameters simultaneously. Water turbidity features beyond the traditional impact factors are incorporated in these regression models. Compared with log-ratio, multi-band and classical machine learning methods, the new approach produced the most accurate results with RMSE value is 0.85 m, in terms of different sediments and water depths combined with in-situ observations of airborne laser bathymetry and multi-beam echo sounder.

2023-02-18 上传