Advanced techniques for hyperspectral/LiDAR sensor data integration

高光谱/激光雷达传感器数据集成的先进技术

• 批准号: RGPIN-2020-05857
• 负责人: AbdElrahman, Ahmed
• 金额: $ 3.13万
• 依托单位: Ryerson University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=749290
• 关键词: Advanced; techniques; hyperspectral; LiDAR; sensor

Integrated hyperspectral and LiDAR data will be an essential tool for a wide variety of environmental and transportation engineering applications. LiDAR data provide primarily the 3D geometry of the scanned environment while hyperspectral sensors deliver data on the radiometric aspects of the surveyed objects. Multi-sensor integration has been one of the active research topics for several years. However, many challenges have not been solved yet; particularly, when data collected from LiDAR and hyperspectral sensors are integrated. This is due to the differences of the sensor data collection techniques and how the data collected is processed. This proposed research program focuses on the development of innovative techniques and technologies for multi-sensor integration and data processing. The program main objectives are: a) Develop low-cost light-weight 3D-hyperspectral mapping system with platform-switching capability for indoor and outdoor mapping; b) Develop new approaches and algorithms for indoor 3D modelling and mapping; and c) Develop genetic algorithms for point cloud classification and feature extraction. The developed all-in-one 3D-hyperspectral mapping system will allow users to switch collection modes (i.e. from airborne, terrestrial, and mobile platforms) with a "plug-and-go" concept. An innovative software will be developed to automatically integrate, for the first time, data captured by LiDAR, and hyperspectral sensors. A new approach will be developed for indoor mapping and 3D modelling without any dependence on indoor infrastructure (e.g. WiFi signal) or GNSS signals. New algorithms will be further developed to further improve the automation of information extraction based on the data collected by the multi-sensor systems. The techniques developed will serve in variety of applications in emerging environment and transportation Monitoring and Modelling (ETMM). Primarily focus will be given to smart city 3D modelling, noise mapping, indoor mapping for mining, and road quality detection. The proposed research program is the first of its kind at inventing a light-weight 3D-hyperspectral mapping system with platform switching capability associated with the corresponding data processing algorithms for knowledge discovery. The proposed research work will serve as a core for a number of fundamental research studies yet to come to facilitate the use of LiDAR and hyperspectral data processing for engineering applications. The research will potentially support technology transfer and commercialisation to the industry and consolidates the training of HQP.

集成的高光谱和激光雷达数据将成为各种环境和运输工程应用的重要工具。LiDAR数据主要提供扫描环境的3D几何形状，而高光谱传感器提供有关被测物体辐射方面的数据。多传感器集成是近年来的研究热点之一。然而，许多挑战尚未得到解决;特别是当从LiDAR和高光谱传感器收集的数据被整合时。这是由于传感器数据收集技术的差异以及如何处理收集的数据。该研究计划的重点是开发多传感器集成和数据处理的创新技术和技术。该方案的主要目标是：（a）开发低成本轻型三维超光谱测绘系统，具有室内和室外测绘平台转换能力;（B）开发室内三维建模和测绘的新方法和算法;（c）开发点云分类和特征提取的遗传算法。 开发的一体化三维高光谱测绘系统将允许用户以“即插即用”的概念切换收集模式（即从机载、地面和移动的平台）。将开发一种创新的软件，首次自动整合LiDAR和高光谱传感器捕获的数据。将为室内测绘和三维建模开发一种新的方法，而不依赖室内基础设施（例如WiFi信号）或全球导航卫星系统信号。将进一步开发新的算法，以进一步提高基于多传感器系统收集的数据的信息提取的自动化。所开发的技术将在新兴的环境和运输监测和建模（ETMM）的各种应用。重点将放在智慧城市3D建模、噪声映射、采矿室内映射和道路质量检测上。拟议的研究计划是第一次在发明一个轻量级的三维高光谱测绘系统与平台切换能力与相应的数据处理算法的知识发现。拟议的研究工作将作为一些基础研究的核心，以促进激光雷达和高光谱数据处理在工程应用中的使用。该研究将有可能支持技术转移和商业化，并巩固HQP的培训。