基于LiDAR-高光谱数据特征表示方法的点云标记

发布时间：2023-03-22 22:13

　　近来,科技的飞速发展使地理空间专业人士能够远程收集特定目标上的多种类型的传感器数据。这些数据带来的挑战与机遇并存。一方面,数据的异质性给它们的高效处理带来了挑战,但另一方面,多源数据集的涌入和可用性也为组合利用异源和多模式数据提供了新的机会,从而使地理空间应用的结果得到改善。LiDAR数据可提供丰富的空间/几何信息,但由于缺乏光谱信息,其在(复杂)城市场景识别任务(如分类)的应用范围有限。高光谱影像数据可提供丰富的光谱信息,但会带来许多问题,例如该数据缺少高程信息,同时训练样本的可用性有限以及来自成像传感器和环境的噪声影响等问题,从而使高光谱数据的分类成为挑战。考虑到各个传感器数据的局限性和功能,以及分类算法设计的本质是提取出有效且区分度高的特征。本研究旨在利用原始LiDAR点云数据并结合该区域的高光谱数据,基于深度学习的时空光谱特征表示方法获取城市场景中目标的最佳特征从而进行分类,并将分类结果标注在原始LiDAR点云上。本文提出了用于初始高光谱特征提取的类-波段选择和降维方法(CBSR)。然后通过双分支卷积高斯-伯努利深度置信网络(CGBDBN),从LiDAR和高光谱图像数据中提取深...

【文章页数】：88 页

【学位级别】：硕士

【文章目录】：
Abstract
摘要
List of Acronyms
1 Introduction
    1.1 Background
    1.2 The State-of-the-art of Point Cloud Labelling
    1.3 Research Content,Workflow and Innovation
        1.3.1 Objectives
        1.3.2 Content
        1.3.3 Workflow
        1.3.4 Innovation
    1.4 Thesis Outline
2 Spatial and Spectral Feature Representation
    2.1 Various Existing Feature Representations
        2.1.1 LiDAR-based features
        2.1.2 Hyperspectral Image-based features
    2.2 Representation Methods
        2.2.1 Classical methods
        2.2.2 Deep learning(DL)methods
    2.3 Datasets
        2.3.1 Data composition and pre-processing
    2.4 Proposed Approach:CBSR for Spectral Feature Representation
    2.5 Proposed Approach:CGBDBN for Spatial Feature Representation
        2.5.1 GBRBMs based on traditional RBMs
        2.5.2 Convolutional GBRBMs
        2.5.3 Convolutional Gaussian-Bernoulli Deep Belief Network
3 Spatial-Spectral Data Fusion and Classification
    3.1 Data Fusion
    3.2 Classification
    3.3 Proposed Method:Spatio-Spectral Feature Representation and Classification through Ensemble Model Stacking
    3.4 2D to3D Projection
4 Experimental Results and Discussion
    4.1 Experimental Setup
        4.1.1 Spatial model
        4.1.2 Spectral model
    4.2 Comprehensive Classification Results
        4.2.1 Houston dataset
        4.2.2 MUUFL Gulfport dataset
    4.3 2D to3D Projection
    4.4 Discussions
        4.4.1 Effects of the image feature patch size
        4.4.2 Performance comparison of the CBSR method to some existing methods
        4.4.3 Performance comparison of the proposed framework to existing methods
        4.4.4 Comparison of ensemble stacking to weighted class probability averaging
        4.4.5 The impact of spatial(DL(L))and spectral(DL(H))feature representations on the proposed spatio-spectral output
5 Conclusions and Recommendation
Acknowledgement
References
Appendix



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