高分辨率遥感图像多时相对齐与分类技术研究

Abstract

Multi-temporal images classification mainly uses a labeled image to classify unlabeled image which is collected at other time. Multitemporal classification includes multitemporal alignment and aligned data classification. Among them, multitemporal alignment is relative to the spectral drift problem caused by the different imaging environment between different remote sensing images. By changing the statistical distribution, the distribution difference between the same objects in the different temporal is eliminated, and the multitemporal image can be used together. Multitemporal classification effectively solves the problem of image interpretation without labels of unclassified image. In recent years, with the development of remote sensing technology, the spatial resolution of remote sensing images has been increasing. For classifying multitemporal remote sensing images under high spatial resolution, besides the existed spectral drift, there are many other problems: data massing caused by the imaging conditions, large internal divergence, low separability of statistical distribution and multi-model in the high resolution images, and the expression of objects under high resolution from traditional pixels to patch or objects. In order to solve the problem of high resolution multi-temporal image classification, this paper studies three aspects, namely, high resolution multi-temporal missing data recovery, drift alignment between different multi-temporal high resolution images, and multiple kernel classification and sparse multiple kernel learning for the aligned data classification. The main research results of this paper are as following:

(1) In order to solve the missing data problem of multitemporal images due to imaging conditions, a multitemporal missing data recovery method without reference is studied by using the correlation characteristic of temporal-spectral. First, the concept of temporal spectral angle for multi-temporal remote sensing images and the corresponding calculation method are constructed. On this basis, a full scene oriented fast similarity search and information recovery algorithm is designed, and a high precision large area cloud-cover data recovery algorithm is realized, which can effectively solve the missing data problem existed in multi-temporal image classification.

(2) Aiming at the problem of low spectral bands, complicated statistical distribution and serious spectral drift, a multitemporal manifold alignment method with label-based topology optimization is studied. First, on the basis of an unsupervised alignment method with explicit projection, based on the label information of the source phase, a majority

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voting method combining the similar weight is constructed to count the largest connection classes in the topology structure and delete the error class connection to achieve the optimization of the manifold topology in the alignment framework. Beside, an improved similarity measurement is proposed to optimize the alignment performance of same scene multitemporal images

(3) For the multidimensional changes of multitemporal HR remote sensing images under multimode observation (multi angle, multi-resolution, multi source and so on), with the spatial-spectral structure of the high resolution remote sensing scene objects themselves, a multi-dimensional cooperative alignment for local space spectrum data is studied to align high resolution multitemporal multimode images. First, based on the tensor Patch data with spatial-spectral structure, multitemporal data is mapped to the corresponding tensor subspace. By constructing a multi-dimensional mapping association matrix in the tensor subspace, multi-dimensional alignment of multitemporal Patch data is realized. Secondly, a multichannel maximum likelihood method for estimating the intrinsic dimensions of the tensor data is proposed. The estimation algorithm is used to estimate the optimal parameters in tensor alignment and reduce the computational complexity of the tensor alignment model.

(4) In view of the transformation from pixel to object in the classification of high resolution multitemporal remote sensing image, an object-based multitemporal alignment method is studied. First, multitemporal image is segmented by SLIC algorithm. Based on the feature of the center point spectrum of the superpixel, the object-based multitemporal alignment is realized by label-based topology optimization manifold alignment. Further, for same scene multitemporal image, by using supervoxel segmentation which keeps the unchanged objects consistency. A temporal-spatial supervoxel segmentation algorithm is proposed to improve the accuracy of alignment of objects under the same scene.

(5) Aiming at the complex distribution of data after alignment, the nonlinearity of remote sensing images still exists. In order to further improve classificationa ccuracy of aligned HR images, a sparse multiple kernel learning (SparseMKL) is studied. Through mappings of the aligned data with multiple kernels and the sparse PCA of the weighted fusion kernel matrix, the sparse maximum variance projection in the fusion kernel matrix is obtained. Thus the sparse scales (best sparse kernels) are obtained, and the selection of the effective kernels and the elimination of the redundant kernels are complete.

Keywords:High spatial resolution remote sensing images; Multitemporal remote sensing; Missing data recovery; Multitemporal Alignment; Multitemporal Classification

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目录

摘要 ................................................................................................................................. I Abstract ........................................................................................................................... III 第1章绪论 . (1)

1.1 课题背景及研究目和意义 (1)

1.1.1 课题背景 (1)

1.1.2 课题研究的目的和意义 (3)

1.2 研究现状 (4)

1.2.1 多时相多光谱图像缺失数据恢复 (4)

1.2.2 多时相多光谱遥感图像对齐 (6)

1.2.3 多时相遥感图像地物分类 (9)

1.2.4 存在问题及解决方法 (11)

1.3 论文主要研究内容与结构安排 (15)

第2章多时相遥感图像缺失数据恢复 (17)

2.1 引言 (17)

2.2 时相-光谱角定义、计算与验证 (18)

2.2.1 多时相遥感图像多维度信息联合表示 (18)

2.2.2 时相-光谱角函数定义与计算 (19)

2.2.3 时相-光谱角函数验证 (20)

2.3 基于时相-光谱角度量的多时相缺失数据恢复 (22)

2.3.1 待恢复像素提取 (22)

2.3.2 基于加权时相-光谱角的最佳相似点搜索 (23)

2.3.3 缺失数据恢复 (25)

2.4 实验结果与分析 (26)

2.4.1 实验数据、对比方法与评价指标 (26)

2.4.2 时相-光谱角函数验证 (27)

2.4.3 仿真缺失数据验证 (28)

2.4.4 真实缺失数据恢复验证 (32)

2.4.5 时相数量、缺失比例与恢复精度关系验证 (33)

2.5 本章小结 (35)

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第3章基于拓扑结构标签优化的多时相遥感图像对齐 (36)

3.1 引言 (36)

3.2 无监督多时相流形对齐 (36)

3.2.1 传统的实例流形对齐 (36)

3.2.2 无监督非实例流形对齐 (37)

3.3 基于拓扑结构标签优化的多时相流形对齐 (38)

3.3.1 拓扑结构标签优化流形对齐 (38)

3.3.2 基于邻近关系的同源场景多时相对齐改进 (40)

3.4 实验结果与分析 (41)

3.4.1 实验数据、对比方法与评价指标 (41)

3.4.2 对齐性能验证 (43)

3.4.3 多时相分类能力验证 (48)

3.4.4 参数影响分析 (55)

3.5 本章小结 (56)

第4章基于空谱联合张量分析的多维度遥感图像对齐 (57)

4.1 引言 (57)

4.2 基于张量子空间对齐的多时相空-谱一体化对齐 (57)

4.2.1 子空间对齐 (57)

4.2.2 张量代数 (58)

4.2.3 张量子空间对齐 (59)

4.3 张量子空间维数估计 (63)

4.4 实验结果与分析 (64)

4.4.1 实验数据、对比方法与评价指标 (64)

4.4.2 同源场景高分辨率多时相对齐分类验证 (65)

4.4.3 异源场景高分多时相对齐分类验证 (74)

4.4.4 多通道本征维度估计验证 (77)

4.5 本章小结 (79)

第5章面向对象的多时相遥感图像对齐 (80)

5.1 引言 (80)

5.2 面向对象的多时相对齐 (80)

5.2.1多时相超像素分割 (80)

5.2.2 超像素特征提取与对象对齐 (82)

5.2.3 基于超体素分割的同源场景多时相对齐 (83)

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5.3 实验结果与分析 (85)

5.3.1 实验数据、对比方法与评价指标 (85)

5.3.2 超像素/超体素分割验证 (85)

5.3.3 基于超像素的多时相分类验证 (89)

5.3.4 基于超体素的多时相分类验证 (93)

5.4 本章小结 (95)

第6章多时相对齐数据的稀疏多核分类 (96)

6.1 引言 (96)

6.2 面向对齐数据的稀疏多核学习 (96)

6.2.1 核方法与支持向量机 (96)

6.2.2 一般多核分类器框架 (98)

6.2.3 核尺度的稀疏特性 (98)

6.2.4 稀疏多核学习 (100)

6.3 实验结果与分析 (102)

6.3.1 实验数据、对比方法与评价指标 (102)

6.3.2 面向对齐数据的分类性能验证 (103)

6.3.2 面向对齐数据的核尺度选择分析 (105)

6.4 本章小结 (107)

结论 (108)

参考文献 (110)

攻读博士期间所发表的论文及其他成果 (123)

哈尔滨工业大学学位论文原创性声明和使用权限 (125)

致谢 (126)

个人简历 (127)

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Contents

Abstract (In Chinese) ........................................................................................................ I Abstract (In English)...................................................................................................... III Chapter 1 Introduction . (1)

1.1 Motivation, objective and significance of the dissertation (1)

1.1.1 Background of the dissertation (1)

1.1.2 Objective and significance of the dissertation (3)

1.2 Research status in China and abroad (4)

1.2.1 3-D multitemporal multispectral missing data recovery (4)

1.2.2 3-D multitemporal multispectral data alignment (6)

1.2.3 3-D multitemporal image classification and interpretation (9)

1.2.4 Main problems and resolutions (11)

1.3 Main research contents of this dissertation (15)

Chapter 2 Multitemporal remote sensing image missing data recovery (17)

2.1 Introduction (17)

2.2 Temporal-spectral angle model: definition, calculation and verification (18)

2.2.1 Joint representation of multidimensional information (18)

2.2.2 Definition and calculation for temporal-spectral angle (19)

2.2.3 Verification method for temporal-spectral angle (20)

2.3 Missing data recovery by temporal-spectral angle estimation (22)

2.3.1 Finding the pixel which need to be recovered (22)

2.3.2 Searching the most similar pixel by using weighted temporal-spectral angle 23

2.3.3 Missing data recovery (25)

2.4 Experimental results and analysis (26)

2.4.1 Experimental data, comparison methods and evaluation indexes (26)

2.4.2 Verifying temporal-spectral angle functions (27)

2.4.3 Experimental results on simulated missing data (28)

2.4.4 Experimental results on real missing data (32)

2.4.5 Relation between temporal number, missing ratio and recovery precision (33)

2.5 Summary (35)

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Chapter 3 Multitemporal remote sensing images alignment by label-based topology optimization (36)

3.1 Introduction (36)

3.2 Unsupervised multitemporal manifold alignment (36)

3.2.1 Traditional pixel-pair based manifold alignment (36)

3.2.2 Generalized unsupervised manifold alignment (37)

3.3 Multitemporal manifold alignment by label-based topology optimization (38)

3.3.1 Manifold alignment by label-based topology optimization (38)

3.3.2 Improved same scene multitemporal alignment with adjacent relationship (40)

3.4 Experimental results and analysis (41)

3.4.1 Experimental data, comparison methods and evaluation indexes (41)

3.4.2 Alignment performance verification (43)

3.4.3 Multitemporal classification verification (48)

3.4.4 Parameter influence analysis (55)

3.5 Summary (56)

Chapter 4 Multitemporal remote sensing images multi-dimension alignment by spatial-spectral joint tensor analysis (57)

4.1 Introduction (57)

4.2 Spatial-spectral co-alignment by tensor subspace alignment (57)

4.2.1 Subspace alignment (57)

4.2.2 Tensor algebra (58)

4.2.2 Tensor subspace alignment (59)

4.3 Tensor subspace dimension estimation (63)

4.4 Experimental results and analysis (64)

4.4.1 Experimental data, comparison methods and evaluation indexes (64)

4.4.2 Alignment performance verification on same scene multitemporal images (65)

4.4.3 Alignment performance verification on cross scene multitemporal images (74)

4.4.4 Multi-channel intrinsic dimension estimation verification (77)

4.5 Summary (79)

Chapter 5 Object-based multitemporal remote sensing images alignment (80)

5.1 Introduction (80)

5.2 Object-based Multitemporal alignment (80)

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5.2.1 Multitemporal superpixel segmentation (80)

5.2.2 Features obtained from superpixel and object alignment (82)

5.2.3 Same scene multitemporal alignment by supervoxel segmentation (83)

5.3 Experimental results and analysis (85)

5.3.1 Experimental data, comparison methods and evaluation indexes (85)

5.3.2 Superpixel/Supervoxel segmentation verification (85)

5.3.3 Multitemporal classification verification based on superpixel (89)

5.3.4 Multitemporal classification verification based on supervoxel (93)

5.4 Summary (95)

Chapter 6 Sparse multiple kernel learning for aligned multitemporal data classification (96)

6.1 Introduction (96)

6.2 Sparse multiple kernel learning for aligned data classification (96)

6.2.1 Kernel method and support vector machines (96)

6.2.2 Traditional multiple kernel learning framework (98)

6.2.3 sparse nature of kernel scales (98)

6.2.4 Sparse multiple kernel learning (100)

6.3 Experimental results and analysis (102)

6.3.1 Experimental data, comparison methods and evaluation indexes (102)

6.3.2 Classification performance verification on aligned data (103)

6.3.3 Kernel scales analysis on aligned data (105)

6.4 Summary (107)

Conclusion (108)

References (110)

List of published papers and other achievements (123)

Announcement of copyright and usage rights (125)

Acknowledgements (126)

Resume (127)

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