Developing full waveform, Bayesian analysis for Multi-Spectral Canopy LiDAR (MSCL) images

为多光谱冠层 LiDAR (MSCL) 图像开发全波形贝叶斯分析

• 批准号: EP/H022414/1
• 负责人: Andrew Wallace
• 金额: $ 16.08万
• 依托单位: Heriot-Watt University
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2010
• 资助国家: 英国
• 起止时间: 2010 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FH022414%2F1
• 关键词: Developing; full; waveform; Bayesian; analysis

We aim to develop image and signal processing algorithms for a new type of air or space borne, remote sensing, 3D imaging LiDAR system designed to measure forest photosynthetic activity in three dimensions. We want to perform full waveform, multi-spectral signal analysis to conduct detailed structural and physiological measurements on forest ecosystems. The requirement is to better interpret data describing the geometry (forest canopy height, height profile, and fractional cover) and physiological signature (photosynthesis, transpiration, somatal response) of trees and vegetation above the earth's surface. By providing better understanding of the data collected from aerial and satellite imaging of forest ecosystems, the proposed research will allow us to better monitor landscape dynamics and the carbon cycle, which is a key factor in the prediction of climate change.Our project is a discipline 'hop' and has 5 phasesPhase 1: Establishing an Inter-Disciplinary Programme: This is a period of familiarisation as the applicant works with researchers at the Edinburgh Earth Observatory (EEO) to better understand the instruments and use of existing software for measurement and interpretation. Phase 2: Developing Bayesian techniques for processing Multi-spectral Canoy LiDAR (MCSL) data: we shall then extend and apply existing processing techniques to the remotely sensed LiDAR imagery to see whether we can gain significant improvement in structural imagery. Allied to this, we should investigate the use of better structural models for the forest canopy scenario, and so develop the algorithms. Task 3: Encoding the algorithms for use within EEO instruments: We need to incorporate the mutual information inherent in several wavelengths in reconstructing better MCSL imagery. The reconstruction of spatial structure and the reflectance analysis (classification) becomes one of drawing posterior inferences from data. Given the mathematical model that we propose, a significant activity will be the development of well structured and documented code to process the LiDAR data. As the project proceeds, we need to encode and document the original software developed in this project so it can be readily used by other researchers.Task 4: Evaluation and trials: As we develop the methodology, we need to assess its effectiveness on data provided by EEO. Structurally, we need to assess whether we can create more accurate 3D forest canopy and ground structure in the presence of significant visual 'clutter' and other confusing factors. Spectrally, we must go beyond current practice in extracting useful data from a series of spectral profiles. Throughout the proposed programme, EOl will be carrying out laboratory and field investigations with MCSL instruments, both existing and new. Measurements will be carried out over an extensive wavelength range in the range 0.4-2.5um using contrasting vegetation types at different growth stages, to be examined over time with different hydrological conditions to observe hyperspectral backscatter.Task 5: Pump-priming and collaboration: A key task will be to bring together the signal processing and geoscience communities to develop further cross-disciplinary activities. At HWU and within the ERPem pooling inititiative (www.erp.ac.uk) we have many staff studying the theory of signal processing in a single and several dimensions, the representation and modelling of sensors and scenes, innovative image and signal processing technologies, image and signal controlled autonomous systems, and systems that model the human-technology collaboration. We would organise pump-priming workshops on key problems with in-house and invited speakers, followed by break-out sessions to develop research and technology transfer proposals. The applicant would assume primary responsibility for their organisation, in consultation with academic staff at the home and host institutions.

我们的目标是为新型空载或星载遥感 3D 成像 LiDAR 系统开发图像和信号处理算法，该系统旨在测量三维森林光合作用活动。我们希望进行全波形、多光谱信号分析，以对森林生态系统进行详细的结构和生理测量。要求是更好地解释描述地球表面以上树木和植被的几何特征（森林冠层高度、高度剖面和覆盖率）和生理特征（光合作用、蒸腾作用、躯体反应）的数据。通过更好地了解从森林生态系统的航空和卫星成像收集的数据，拟议的研究将使我们能够更好地监测景观动态和碳循环，这是预测气候变化的关键因素。我们的项目是一门学科“跳跃”，有 5 个阶段第 1 阶段：建立跨学科计划：这是一个熟悉阶段，申请人与爱丁堡地球观测站 (EEO) 的研究人员合作，以更好地了解用于测量和测量的仪器和现有软件的使用。 解释。第 2 阶段：开发用于处理多光谱 Canoy LiDAR (MCSL) 数据的贝叶斯技术：然后，我们将扩展现有的处理技术并将其应用到遥感 LiDAR 图像，看看我们是否能够在结构图像方面获得显着改进。与此相关的是，我们应该研究在森林冠层场景中使用更好的结构模型，从而开发算法。任务 3：对 EEO 仪器中使用的算法进行编码：我们需要结合多个波长固有的互信息来重建更好的 MCSL 图像。空间结构的重建和反射率分析（分类）成为从数据中进行后推论的一种。鉴于我们提出的数学模型，一项重要的活动将是开发结构良好且记录良好的代码来处理激光雷达数据。随着项目的进展，我们需要对本项目中开发的原始软件进行编码和记录，以便其他研究人员可以轻松使用。任务 4：评估和试验：在开发方法时，我们需要评估其对所提供数据的有效性由平等就业机会。从结构上讲，我们需要评估在存在明显的视觉“混乱”和其他令人困惑的因素的情况下是否可以创建更准确的 3D 森林冠层和地面结构。在光谱方面，我们必须超越当前从一系列光谱剖面中提取有用数据的做法。在整个拟议计划中，EOl 将使用 MCSL 仪器（包括现有的和新的）进行实验室和现场调查。测量将在 0.4-2.5um 范围内的广泛波长范围内进行，使用不同生长阶段的对比植被类型，随着时间的推移在不同的水文条件下进行检查，以观察高光谱反向散射。任务 5：泵启动和协作：A关键任务是将信号处理和地球科学界聚集在一起，开展进一步的跨学科活动。在 HWU 和 ERPem 池计划 (www.erp.ac.uk) 内，我们有许多工作人员研究单个维度和多个维度的信号处理理论、传感器和场景的表示和建模、创新图像和信号处理技术、图像和信号控制的自主系统以及模拟人类技术协作的系统。我们将与内部和受邀演讲者就关键问题组织泵启动研讨会，然后举行分组会议以制定研究和技术转让建议。申请人将与本国和主办机构的学术人员协商，承担其组织的主要责任。

项目成果

Design and Evaluation of Multispectral LiDAR for the Recovery of Arboreal Parameters

• DOI: 10.1109/tgrs.2013.2285942
• 发表时间: 2014-08-01
• 期刊: IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
• 影响因子: 8.2
• 作者: Wallace, Andrew M.;McCarthy, Aongus;Buller, Gerald S.
• 通讯作者: Buller, Gerald S.

Multispectral single-photon detection in time-of-flight depth profiling

飞行时间深度分析中的多光谱单光子检测

• 发表时间: 2014
• 期刊: Institute of Physics Conference on Photonics
• 作者: Ren, X

Full Waveform Analysis for Long-Range 3D Imaging Laser Radar

• DOI: 10.1155/2010/896708
• 发表时间: 2010-01-01
• 期刊: EURASIP JOURNAL ON ADVANCES IN SIGNAL PROCESSING
• 影响因子: 1.9
• 作者: Wallace, Andrew M.;Ye, Jing;Buller, Gerald S.
• 通讯作者: Buller, Gerald S.

Developing Hyperspectral LiDAR for Structural and Biochemical Analysis of Forest Data

• 发表时间: 2012-11
• 作者: D. Martinez-Ramirez;G. Buller;A. Mccarthy;Ximing Ren;Andrew M. Wallace;S. Morak;Caroline Nichol;Iain H. Woodhouse

Recovery of Forest Canopy Parameters by Inversion of Multispectral LiDAR Data

• DOI: 10.3390/rs4020509
• 发表时间: 2012-02
• 期刊: Remote. Sens.
• 作者: Andrew M. Wallace;C. Nichol;Iain H. Woodhouse