遥感影像的张量表达与流形学习方法研究
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摘要
快速、准确地从遥感影像中提取感兴趣的关键信息是遥感对地观测领域的重要研究内容。随着当代遥感传感器技术呈现出高光谱分辨率、高空间分辨率和高时间分辨率的新特点,传统的人工解译遥感影像的方式已经完全不适用,取而代之的是计算机全自动提取遥感影像信息的新方法。虽然现在计算机硬件的发展已经为快速、准确处理海量遥感影像提供了物理基础,但是相应的数据处理算法普遍存在自适应能力不足的缺点。因此,本论文基于机器学习与模式识别领域的新理论,以数据的张量表达和流形学习为研究主线,结合正则化理论、稀疏学习、迁移学习等新方法,开展遥感影像信息提取中的流形学习方法研究。主要研究内容如下：
(1)系统地介绍了流形学习的基本原理与分类,回顾了流形学习方法的发展历史,并以片排列框架为基础,详细给出了经典数据降维算法PCA、LDA和流形学习的代表性算法LLE、ISOMAP、LE、LTSA、HLLE的核心思想及目标函数的局部相关性矩隈构建过程,并对各种算法进行了比较。片排列框架使得我们从新的角度深入理解了流形学习方法,并且为根据实际问题生成更有效的流形学习算法提供了基础；
(2)对遥感影像的张量分析方法进行了深入的研究。首先具体介绍了张量的定义及相关的多维线性代数理论；接着提出了遥感影像时间-空间-光谱特征一体化张量描述的理论；随后,提出了一种在张量流形空间中对“双高”遥感影像特征提取的算法：张量判别局部排列,该方法将张量表达方法与流形学习理论结合,针对“双高”影像的数据特点进行特征提取,有效的提高了新特征的判别力和对遥感影像的分类精度。
(3)提出了两种多特征融合的自适应流形学习框架。两种方法分别基于拉普拉斯特征映射和随机邻域嵌入构建样本之间的局部相关性矩阵。随后,为了将融合问题中不同特征的权重作为参数加入优化函数,分别使用了两种正则化方法。值得注意的是,以上两种方法都是自适应的多特征融合框架算法,即该算法能够用于任意多种特征融合问题并根据目标函数自适应地找到每种特征最优的权重；
(4)利用机器学习领域的最新成果,提出了两种多约束的判别流形学习高光谱目标探测方法：稀疏迁移学习的高光谱目标探测和多约束测度学习的亚像素目标探测。两种方法的共同点是在判别流形学习的框架中加入了对标记样本和无标记样本的多重约束,防止在判别流形学习中对目标样本的过学习。这样,流形学习的方法在多约束的作用下能够有效地用于高光谱遥感影像目标探测；
(5)针对高光谱遥感影像分析与分类问题,提出将模式分析中现有的基于向量的学习机推广为基于张量的学习机的一般框架。在该框架的支持下,提出了多特征支持张量机、多类支持张量机、邻近支持张量机等张量描述的高光谱影像多维线性分析与分类算法,进一步证明了张量描述与张量学习方法的有效性。
It is the main purpose of earth observation to extract the interested information and knowledge from remote sensing (RS) images quickly and accurately. With the development of RS technology, RS images with very high resolution and hyperspectral channels have been able to provide a large amount of information, we have much more multispectral, high spatial resolution, and temporal resolution RS data than before. As a result of that, it is inefficient or even impossible to interpret these data by human beings. Thus, with the development of computer hardware, we need to explore some intelligent algorithms to process such mass RS data quickly and accurately. This thesis aims to propose some RS image information extraction algorithms based on the latest methods in machine learning area. In particular, we adopts the manifold learning technology as the mainline and combins the regularization theory, tensor method, sparse learning and transfer learning into our algorithms. The main contributions of this thesis are as follows:
(1) A patch alignment framework is introduced to unify the conventional dimension reduction (DR) methods such as PCA&LDA, and some representative manifold learning algorithms snch as LLE, ISOMAP, LE, LTSA&HLLE into an optimization. This framework reveals the basic principle of manifold learning algorithms, that is1) algorithms are intrinsically different in the patch optimization stage and2) all algorithms share an almost identical whole alignment stage. As an application of this framework, we could develop new dimensionality reduction algorithms by modify the local alignment matrix.
(2) The tensor based manifold learning methods in RS image analysis are extensively discussed. In this paper, we propose amethod for the dimensionality reduction of spectral-spatial features in RS images, under the umbrella of multilinear algebra, i.e., the algebra of tensors. The proposed approach is a tensor extension of conventional supervised manifold learning based DR. In particular, we define a tensor organization scheme for representing a pixel's spectral-spatial feature and develop tensor discriminative locality alignment (TDLA) for removing redundant information for subsequent classification.
(3) Two types of multiple feature combining (MFC) algorithms are proposed to deal with RS image classification tasks with multiple features as input. In these two methods, we use the LE and SNE to bulid the local alignment matrix, respectively. Then, the proposed adaptive manifold learning MFC algorithms combine the input multiple features linearly in the optimal way and obtain a unified low-dimensional representation of these multiple features for subsequent classification. Each feature has its particular contribution to the unified representation determined by simultaneously optimizing the weights in the objective function.
(4) Two types of regularized discriminative manifold learning algorithms are proposed for hyperspectral target detection, named sparse transfer manifold embedding (STME) and supervised metric learning (SML). Technically speaking, these methods are particularly designed for hyperspectral target detection by introducing the multiple constraints into discriminative manifold learning framework. In STME, a sparse formulation and a transfer regularization are adopted, while in SML, a similarity propagation constraint and a manifold smoothness regularization are enforced. Both of the algorithms have showed the outstanding detection performance.
(5) A general multilinear data analysis framework for tensor inputs is investigated for RS image classification. This framework generalizes the current classifiers which only accept vectors as inputs into multilinear condition. Based on this framework, we further propose some algorithms, e.g., support tensor machine, multiclass support tensor machine, and proximal support tensor machine. These methods show the effectiveness of tensor representation and analysis approaches in RS image information extraction with a small number of training samples.
(1)系统地介绍了流形学习的基本原理与分类,回顾了流形学习方法的发展历史,并以片排列框架为基础,详细给出了经典数据降维算法PCA、LDA和流形学习的代表性算法LLE、ISOMAP、LE、LTSA、HLLE的核心思想及目标函数的局部相关性矩隈构建过程,并对各种算法进行了比较。片排列框架使得我们从新的角度深入理解了流形学习方法,并且为根据实际问题生成更有效的流形学习算法提供了基础；
(2)对遥感影像的张量分析方法进行了深入的研究。首先具体介绍了张量的定义及相关的多维线性代数理论；接着提出了遥感影像时间-空间-光谱特征一体化张量描述的理论；随后,提出了一种在张量流形空间中对“双高”遥感影像特征提取的算法：张量判别局部排列,该方法将张量表达方法与流形学习理论结合,针对“双高”影像的数据特点进行特征提取,有效的提高了新特征的判别力和对遥感影像的分类精度。
(3)提出了两种多特征融合的自适应流形学习框架。两种方法分别基于拉普拉斯特征映射和随机邻域嵌入构建样本之间的局部相关性矩阵。随后,为了将融合问题中不同特征的权重作为参数加入优化函数,分别使用了两种正则化方法。值得注意的是,以上两种方法都是自适应的多特征融合框架算法,即该算法能够用于任意多种特征融合问题并根据目标函数自适应地找到每种特征最优的权重；
(4)利用机器学习领域的最新成果,提出了两种多约束的判别流形学习高光谱目标探测方法：稀疏迁移学习的高光谱目标探测和多约束测度学习的亚像素目标探测。两种方法的共同点是在判别流形学习的框架中加入了对标记样本和无标记样本的多重约束,防止在判别流形学习中对目标样本的过学习。这样,流形学习的方法在多约束的作用下能够有效地用于高光谱遥感影像目标探测；
(5)针对高光谱遥感影像分析与分类问题,提出将模式分析中现有的基于向量的学习机推广为基于张量的学习机的一般框架。在该框架的支持下,提出了多特征支持张量机、多类支持张量机、邻近支持张量机等张量描述的高光谱影像多维线性分析与分类算法,进一步证明了张量描述与张量学习方法的有效性。
It is the main purpose of earth observation to extract the interested information and knowledge from remote sensing (RS) images quickly and accurately. With the development of RS technology, RS images with very high resolution and hyperspectral channels have been able to provide a large amount of information, we have much more multispectral, high spatial resolution, and temporal resolution RS data than before. As a result of that, it is inefficient or even impossible to interpret these data by human beings. Thus, with the development of computer hardware, we need to explore some intelligent algorithms to process such mass RS data quickly and accurately. This thesis aims to propose some RS image information extraction algorithms based on the latest methods in machine learning area. In particular, we adopts the manifold learning technology as the mainline and combins the regularization theory, tensor method, sparse learning and transfer learning into our algorithms. The main contributions of this thesis are as follows:
(1) A patch alignment framework is introduced to unify the conventional dimension reduction (DR) methods such as PCA&LDA, and some representative manifold learning algorithms snch as LLE, ISOMAP, LE, LTSA&HLLE into an optimization. This framework reveals the basic principle of manifold learning algorithms, that is1) algorithms are intrinsically different in the patch optimization stage and2) all algorithms share an almost identical whole alignment stage. As an application of this framework, we could develop new dimensionality reduction algorithms by modify the local alignment matrix.
(2) The tensor based manifold learning methods in RS image analysis are extensively discussed. In this paper, we propose amethod for the dimensionality reduction of spectral-spatial features in RS images, under the umbrella of multilinear algebra, i.e., the algebra of tensors. The proposed approach is a tensor extension of conventional supervised manifold learning based DR. In particular, we define a tensor organization scheme for representing a pixel's spectral-spatial feature and develop tensor discriminative locality alignment (TDLA) for removing redundant information for subsequent classification.
(3) Two types of multiple feature combining (MFC) algorithms are proposed to deal with RS image classification tasks with multiple features as input. In these two methods, we use the LE and SNE to bulid the local alignment matrix, respectively. Then, the proposed adaptive manifold learning MFC algorithms combine the input multiple features linearly in the optimal way and obtain a unified low-dimensional representation of these multiple features for subsequent classification. Each feature has its particular contribution to the unified representation determined by simultaneously optimizing the weights in the objective function.
(4) Two types of regularized discriminative manifold learning algorithms are proposed for hyperspectral target detection, named sparse transfer manifold embedding (STME) and supervised metric learning (SML). Technically speaking, these methods are particularly designed for hyperspectral target detection by introducing the multiple constraints into discriminative manifold learning framework. In STME, a sparse formulation and a transfer regularization are adopted, while in SML, a similarity propagation constraint and a manifold smoothness regularization are enforced. Both of the algorithms have showed the outstanding detection performance.
(5) A general multilinear data analysis framework for tensor inputs is investigated for RS image classification. This framework generalizes the current classifiers which only accept vectors as inputs into multilinear condition. Based on this framework, we further propose some algorithms, e.g., support tensor machine, multiclass support tensor machine, and proximal support tensor machine. These methods show the effectiveness of tensor representation and analysis approaches in RS image information extraction with a small number of training samples.
引文
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