基于快速流形学习方法的高光谱遥感非线性特征提取研究
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摘要
高光谱遥感技术是地球观测体系中的重要一员,是遥感技术发展的前沿领域。近年来随着高光谱传感器的不断升级换代,高光谱数据的空间分辨率与光谱分辨率不断升高。这为高光谱遥感数据处理带来了沉重的压力,使得高维空间特征提取技术成为了高光谱遥感数据预处理必不可少的一环。本文欲将一类新型的具备非线性特征提取能力的特征提取方法,流形学习方法,引入高光谱遥感处理中。该类方法结构简单,所需输入参数少,对参数精度依赖度低,并能保证全局最优解,但计算复杂度高,难以处理高光谱传感器带来的海量数据。为了克服这一困难,本文综合分析了两类流形学习方法(全局法与局部法),以其中两个代表性方法为例(等角特征映射法与拉普拉斯特征映射法),建立了统一的流形学习算法架构。在该架构中,分别从近邻搜索和低维坐标解算两个计算复杂度最高的环节入手,先后提出和引入了光谱角敏感哈希森林方法和尼斯特罗姆方法,建立了新的流形学习算法架构,以改进流形学习方法的计算效率。本文不仅从理论上证明,更在实际数据实验中验证了这两种方法与传统相同类型算法相比在计算效率上的优势。为了验证新算法架构的实用性,本文采用了三组基准测试数据,在流形学习特征提取的基础上进行聚类和分类实验,并与主成分分析法的提取结果进行比较。经过详细的比较和参数分析,实验结果表明,新算法架构下的流形学习方法不仅在聚类/分类应用中对主成分分析法保持稳定的优势,在之基础上的聚类/分类结果更加超过了直接在原始数据上的聚类/分类精度。
Hyperspectral Remote Sensing plays an important role in Earth Observation and is a cuting edge technique among all the Remote Sensing applications. The development of the Hyperspectral Sensors accelerates the growth of spacial and spectral resolution of the Hyperspectral Remote Sensed dataset, which burdens the workload of the Hyperspectral data analysis and processes, and helps the Feature Extraction become an indispensible part of the Hyperspectral dataset preprocessing. This thesis proposes a new kind of feature extraction algorithms, as known as Manifold Learning (ML). ML is a kind of nonlinear feature extraction techniques with simple structure, few parameters, and guaranteed globally optimal solution. But its high computational complexisity impedes its development in the Hyperspectral applications where the large scale data size is beyond the processing power of ML. To overcome this problem, an unified framework of the ML algorithm was established after two kinds of ML algorithms were systematically analyzed, globally structure preserved methods and locally structure preserved methods respectively. Spectral Angle Sensitive Hashing Forest (SASHF) and Nystrom' algorithm were introduced to improve the computational efficiency of nearest neighbor searching and low dimensional coordinates calculating which are the most complicated parts of the ML algorithms. In the thesis, these two algorithms were proved theoretically and experimentally that they have the advantages in computing efficiency against other similar algorithms. An improved framwork of ML was built based on SASHF and Nystrom' algorithm. In the experiments of clustering and classification on three sets of benchmark dataset, this new ML algorithm can not only maintain the information of the original dataset, but also help to improve the performance of the clustering/classification algorithm. The features extracted by the new ML algorithm were proved to outperform the features extracted by Principle Component Analysis and even the original dataset itself.
Hyperspectral Remote Sensing plays an important role in Earth Observation and is a cuting edge technique among all the Remote Sensing applications. The development of the Hyperspectral Sensors accelerates the growth of spacial and spectral resolution of the Hyperspectral Remote Sensed dataset, which burdens the workload of the Hyperspectral data analysis and processes, and helps the Feature Extraction become an indispensible part of the Hyperspectral dataset preprocessing. This thesis proposes a new kind of feature extraction algorithms, as known as Manifold Learning (ML). ML is a kind of nonlinear feature extraction techniques with simple structure, few parameters, and guaranteed globally optimal solution. But its high computational complexisity impedes its development in the Hyperspectral applications where the large scale data size is beyond the processing power of ML. To overcome this problem, an unified framework of the ML algorithm was established after two kinds of ML algorithms were systematically analyzed, globally structure preserved methods and locally structure preserved methods respectively. Spectral Angle Sensitive Hashing Forest (SASHF) and Nystrom' algorithm were introduced to improve the computational efficiency of nearest neighbor searching and low dimensional coordinates calculating which are the most complicated parts of the ML algorithms. In the thesis, these two algorithms were proved theoretically and experimentally that they have the advantages in computing efficiency against other similar algorithms. An improved framwork of ML was built based on SASHF and Nystrom' algorithm. In the experiments of clustering and classification on three sets of benchmark dataset, this new ML algorithm can not only maintain the information of the original dataset, but also help to improve the performance of the clustering/classification algorithm. The features extracted by the new ML algorithm were proved to outperform the features extracted by Principle Component Analysis and even the original dataset itself.
引文
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