基于多特征集成的SAR图像分割算法研究
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摘要
随着合成孔径雷达(SAR)系统的迅速发展,获得的SAR图像数据也越来越多,而对SAR图像理解与解译能力则相对滞后。SAR图像分割是SAR图像解译的关键部题之一,也是SAR图像自动理解与解译的基础和前提,受到各国研究者的广泛关注,成为近年来的研究热点。相比于其它种类图像(如光学或红外图像),SAR图像所固有的特点(如相干斑噪声明显,同类目标差异较大,异类目标非常接近,多尺度目标的同时存在,数据规模庞大等)给分割工作带来了严峻的挑战。因此,开展SAR图像分割问题的研究对于促进SAR技术的发展具有重要意义。
本论文针对SAR图像的特点,从特征提取到算法设计,完整的对SAR图像分割问题进行研究,提出了多种基于多特征集成的SAR图像分割算法。本论文主要创新点概括如下：
1.提出了一种基于上下文分析的非均衡合并SAR图像分割算法。该算法采用了自底向上(Bottom-up)和自上而下(Top-down)相结合的策略。在自底向上方面,本算法集成了多种SAR图像特征,这些特征从不同的角度出发来刻画和表示SAR图像中的目标；在自上而下方面,本算法根据格式塔理论,提出了三条规则来对超像素的上下文进行分析,对不同的特征进行组织和集成。这三条规则体现了借鉴于认知心理学的先验知识,实现了一种新颖的、有效的方法来对超像素的上下文进行建模,是对超像素合并的一种自上而下的全局性约束。根据所提出的上下文和多种图像特征,本算法设计了两阶段的合并策略,包括：1)粗合并阶段(Coarse Merging Stage,CMS);2)细合并阶段(Fine Merging Stage,FMS)。这种策略能够在算法的效率和准确性之间做到很好的平衡。实验表明CMS算法能够快速的合并大量具有歧义的超像素,而FMS能够获得更加准确的分割结果。
2.提出了一种基于特征子空间迭代优化的SAR图像分割算法,该算法具有以下特点：1)集成了多种SAR图像特征,以准确的表示SAR图像中的不同目标；2)提出了一种分阶段集成的相似度计算方法,该方法分别在特征水平(Feature Level)和相似度水平(Similarity Level)计算基于独立特征的相似度和基于多特征集成的相似度,从而有效的避免不同特征、不同维数之间的相互影响,提高超像素之间相似度计算的准确性；3)提出了基于偏好的自适应空间约束项,该空间约束项能够根据图像中上下文的内容自适应的计算超像素与其上下文邻域的空间关系,使超像素更接近于上下文中的同类目标,可以避免传统平均策略对图像细节的损失,从而使分割结果更加准确、更具鲁棒性。4)根据构造的目标函数,提出了基于特征子空间的迭代优化算法,该算法能够对不同特征子空间进行迭代优化,从而保持不同属性的特征在其对应子空间中的分布结构,避免相互的干扰,在保持算法分割效率的同时,提高算法迭代优化的准确度。
3.对基于特征域的优化技术进行更深入的研究,提出了两种新的免疫克隆优化算法,分别是：免疫调节克隆选择算法和基于正交试验设计的克隆选择优化算法。然后,根据迭代最小化算法和免疫克隆选择优化算法各自的优势,本文将其进行了有机结合,提出了一种基于特征域的混合优化方法,并将其应用于SAR图像的分割问题中。这种方法既能够在各个特征子空间中沿着梯度最速下降的方向进行迭代搜索,也能够在整个高维空间中进行基于克隆选择优化的全局启发式搜索,从而同时兼有了迭代最小化算法的高效性,以及免疫克隆选择算法的鲁棒性。对比实验表明,这种混合的优化算法能够有效的提高算法的搜索精度,避免算法陷入局部极值,明显的提高了分割算法的性能。
4.提出了一种基于空间域与特征域的无监督分层迭代算法,其主要出发点是结合基于特征域分割算法与基于空间域分割算法各自的优势,取长补短,以达到对SAR图像最佳分割的目的。在合并超像素时,该算法采用了分层迭代的策略：设计了一种改进的模糊c均值聚类算法,对超像素的外观特征进行迭代优化,当得到聚类结果后,对同类超像素的空间上下文进行分析,使用区域增长算法在全局范围内对相似的超像素进行合并,直到不存在满足合并条件的超像素为止,再重新进行聚类。这两个迭代子算法分层交替进行,既可以挖掘特征的分布结构信息,避免欠分割或过分割,又可以有效利用SAR图像的多种信息,提高算法对歧义目标的鲁棒性,从而实现了一种直接的、有效的方式来组织和集成SAR图像多种特征。
5.提出了一种无监督两阶段SAR图像分割方法,对基于空间域的分割算法与基于特征域的分割算法进行结合。该算法包含两个阶段,分别为：1)粗合并阶段；2)细分类阶段。在第一阶段中,本文提出了一种新的基于上下文分析的区域迭代合并(Context-based Region Iterative Merging,CRIM)算法,以利用尽量多种的特征,来对超像素进行合并。该子算法的一个优势是能够快速的、对大量位于真实分割区域内部的超像素进行合并,从而提高特征表示的准确度,减轻细分类算法的计算负担。与传统的区域合并算法相比,CRIM算法能够集成更多的信息,并且采用了一种新颖的全局停止指标来决定CRIM算法的输出。这种指标能够从全局的角度出发,计算SAR图像分割区域的均匀一致性。在细分类阶段,本章采用了之前提出的基于混合优化策略的模糊聚类算法来对图像空间中剩余的超像素进行分类,以达到最终分割的目的。
With the rapid development of synthetic aperture radar (SAR) system, a great number of SAR image data is acquired. On the other side, the research on theory and technique of SAR image understanding and interpretation has fallen behind. SAR image segmentation is one of the key techniques for SAR image automatic interpretation, and is a current research hotspot attracting lots of scholars'attention. Comparing with other kinds of images (e.g., optical images, thermal infrared images, and so on), SAR images own their unique properties, which bring great challenge to the problem of SAR image segmentation. For example, SAR images are in some degree contaminated by speckle noise; SAR images usually consist of multi-scale objects even in the same scene; the exhibition of the same terrain target in SAR images is often nonstationary and has complex variation. Therefore, the research on SAR image segmentation is of great significance for the development of SAR system.
Considering the characteristics of SAR images, this dissertation investigates SAR image segmentation from the feature extraction to the algorithm design, and proposes a few SAR images segmentation algorithms based on multi-feature ensemble, which can be summarized as follows:
1. A context based hierarchical unequally merging for SAR image segmentation is proposed, which combines the top-down fashion and bottom-up fashion. Based on the Gestalt laws, three rules are proposed to represent superpixel context that realize a new and natural way to manage different kinds of features extracted from SAR images. The rules are prior knowledge from cognitive science and serve as top-down constraints to globally guide the superpixel merging. The features, including brightness, texture, edges, and spatial information, locally describe the superpixels of SAR images and are bottom-up forces. While merging superpixels, a hierarchical unequally merging algorithm is designed, which includes two stages:a) coarse merging stage; and b) fine merging stage. The merging algorithm unequally allocates computation resources so as to spend less running time in the superpixels without ambiguity and more running time in the superpixels with ambiguity. Experiments on synthetic and real SAR images indicate that this algorithm can make a balance between computation speed and segmentation accuracy.
2. A fuzzy clustering based on subspace iterative optimization is proposed, which is characterized by following aspects:1) multiple features have been extracted to accurately describe the objects in SAR images.2) A novel similarity measure using hierarchical ensemble is presented, which integrate these features of different properties respectively in the feature level and the similarity level to avoid the mutual influences between features and maximize discrimination ability of the similarity measure between objects.3) A novel spatial neighbourhood term with preferences has been introduced to the objective function, which can compute the contextual relationship between superpixels more accurately and reasonable than the traditional average methods.4) According to the objective function, a subspace iterative optimization algorithm is designed,which can optimize the objective function iteratively in each feature subspace to avoid the mutual influences between different kinds of features and preserve the distribution structures of the data points in every subspace.
3. Immune clonal selection optimization is carefully studied, and we propose two novel immune clonal selection optimization algorithms, which are:immunologic regulation clonal selection algorithm and clonal selection optimization based on orthogonal experiment design. Then, considering the advantages of the alternating minimization and the clonal selection optimization, we combine the two kinds of methods into a framework. The former one can iteratively search along the direction of the gradient steepest descent so as to accelerate the convergence speed of the algorithm; while the latter one can avoid premature convergence and find the global optimal solution with high probability so as to improve the robustness and accuracy of the algorithm.Experiments indicate that the proposed hybrid optimization method can achieve the best performance and is stable and effectiveness for various kinds of SAR images.
4. A context based unsupervised hierarchical iterative algorithm for SAR segmentation is proposed, which combines the advantages of the cluster based segmentation algorithms and region growing based segmentation algorithms.While merging superpixels,this algorithm chooses a hierarchical iterative strategy:a modified fuzzy c-means algorithm is first designed to analyze the appearance-based features of superpixels, and then region iterative growing is used to merge the similar superpixels based on contextual analysis in space domain.After that, a new loop of the clustering algorithm and the region growing begins. These two iterative sub-algorithms perform hierarchically and realize a natural and effective way to use different kinds of information to segment SAR images. Experiments on synthetic and real SAR images indicate that the proposed algorithm can obtain excellent segmentation and make a good balance between region consistency and preserving image details.
5. A two-stage algorithm for SAR image segmentation is presented by combing the advantages of the image-space based method and the feature-space based method. The algorithm consists of two stages:1) coarse merging stage, and2) fine classification stage. A context-based region iterative merging (CRIM) algorithm is proposed in the coarse merging stage, which can use many different kinds of information to guide the superpixel merging. The advantage of CRIM is that it can merge most of the superpixels inside the true segments at a very fast speed, so as to improve the accuracy of features and reduce the computation burden for the fine classification stage. Comparing with the traditional region merging algorithms, CRIM can take use of much more information, and a global halt index (HI) is carefully designed to decide the output of CRIM, which has global consistency in homogeneity. In the fine classification stage, the designed fuzzy clustering using hybrid optimization is adopted to produce the final segmentation result, which realizes the combination of the gradient-descent-direction search and the heuristic search. The former one can accelerate the algorithm's convergence speed; while the latter one can avoid premature and find the global optimal solution with high probability.
本论文针对SAR图像的特点,从特征提取到算法设计,完整的对SAR图像分割问题进行研究,提出了多种基于多特征集成的SAR图像分割算法。本论文主要创新点概括如下：
1.提出了一种基于上下文分析的非均衡合并SAR图像分割算法。该算法采用了自底向上(Bottom-up)和自上而下(Top-down)相结合的策略。在自底向上方面,本算法集成了多种SAR图像特征,这些特征从不同的角度出发来刻画和表示SAR图像中的目标；在自上而下方面,本算法根据格式塔理论,提出了三条规则来对超像素的上下文进行分析,对不同的特征进行组织和集成。这三条规则体现了借鉴于认知心理学的先验知识,实现了一种新颖的、有效的方法来对超像素的上下文进行建模,是对超像素合并的一种自上而下的全局性约束。根据所提出的上下文和多种图像特征,本算法设计了两阶段的合并策略,包括：1)粗合并阶段(Coarse Merging Stage,CMS);2)细合并阶段(Fine Merging Stage,FMS)。这种策略能够在算法的效率和准确性之间做到很好的平衡。实验表明CMS算法能够快速的合并大量具有歧义的超像素,而FMS能够获得更加准确的分割结果。
2.提出了一种基于特征子空间迭代优化的SAR图像分割算法,该算法具有以下特点：1)集成了多种SAR图像特征,以准确的表示SAR图像中的不同目标；2)提出了一种分阶段集成的相似度计算方法,该方法分别在特征水平(Feature Level)和相似度水平(Similarity Level)计算基于独立特征的相似度和基于多特征集成的相似度,从而有效的避免不同特征、不同维数之间的相互影响,提高超像素之间相似度计算的准确性；3)提出了基于偏好的自适应空间约束项,该空间约束项能够根据图像中上下文的内容自适应的计算超像素与其上下文邻域的空间关系,使超像素更接近于上下文中的同类目标,可以避免传统平均策略对图像细节的损失,从而使分割结果更加准确、更具鲁棒性。4)根据构造的目标函数,提出了基于特征子空间的迭代优化算法,该算法能够对不同特征子空间进行迭代优化,从而保持不同属性的特征在其对应子空间中的分布结构,避免相互的干扰,在保持算法分割效率的同时,提高算法迭代优化的准确度。
3.对基于特征域的优化技术进行更深入的研究,提出了两种新的免疫克隆优化算法,分别是：免疫调节克隆选择算法和基于正交试验设计的克隆选择优化算法。然后,根据迭代最小化算法和免疫克隆选择优化算法各自的优势,本文将其进行了有机结合,提出了一种基于特征域的混合优化方法,并将其应用于SAR图像的分割问题中。这种方法既能够在各个特征子空间中沿着梯度最速下降的方向进行迭代搜索,也能够在整个高维空间中进行基于克隆选择优化的全局启发式搜索,从而同时兼有了迭代最小化算法的高效性,以及免疫克隆选择算法的鲁棒性。对比实验表明,这种混合的优化算法能够有效的提高算法的搜索精度,避免算法陷入局部极值,明显的提高了分割算法的性能。
4.提出了一种基于空间域与特征域的无监督分层迭代算法,其主要出发点是结合基于特征域分割算法与基于空间域分割算法各自的优势,取长补短,以达到对SAR图像最佳分割的目的。在合并超像素时,该算法采用了分层迭代的策略：设计了一种改进的模糊c均值聚类算法,对超像素的外观特征进行迭代优化,当得到聚类结果后,对同类超像素的空间上下文进行分析,使用区域增长算法在全局范围内对相似的超像素进行合并,直到不存在满足合并条件的超像素为止,再重新进行聚类。这两个迭代子算法分层交替进行,既可以挖掘特征的分布结构信息,避免欠分割或过分割,又可以有效利用SAR图像的多种信息,提高算法对歧义目标的鲁棒性,从而实现了一种直接的、有效的方式来组织和集成SAR图像多种特征。
5.提出了一种无监督两阶段SAR图像分割方法,对基于空间域的分割算法与基于特征域的分割算法进行结合。该算法包含两个阶段,分别为：1)粗合并阶段；2)细分类阶段。在第一阶段中,本文提出了一种新的基于上下文分析的区域迭代合并(Context-based Region Iterative Merging,CRIM)算法,以利用尽量多种的特征,来对超像素进行合并。该子算法的一个优势是能够快速的、对大量位于真实分割区域内部的超像素进行合并,从而提高特征表示的准确度,减轻细分类算法的计算负担。与传统的区域合并算法相比,CRIM算法能够集成更多的信息,并且采用了一种新颖的全局停止指标来决定CRIM算法的输出。这种指标能够从全局的角度出发,计算SAR图像分割区域的均匀一致性。在细分类阶段,本章采用了之前提出的基于混合优化策略的模糊聚类算法来对图像空间中剩余的超像素进行分类,以达到最终分割的目的。
With the rapid development of synthetic aperture radar (SAR) system, a great number of SAR image data is acquired. On the other side, the research on theory and technique of SAR image understanding and interpretation has fallen behind. SAR image segmentation is one of the key techniques for SAR image automatic interpretation, and is a current research hotspot attracting lots of scholars'attention. Comparing with other kinds of images (e.g., optical images, thermal infrared images, and so on), SAR images own their unique properties, which bring great challenge to the problem of SAR image segmentation. For example, SAR images are in some degree contaminated by speckle noise; SAR images usually consist of multi-scale objects even in the same scene; the exhibition of the same terrain target in SAR images is often nonstationary and has complex variation. Therefore, the research on SAR image segmentation is of great significance for the development of SAR system.
Considering the characteristics of SAR images, this dissertation investigates SAR image segmentation from the feature extraction to the algorithm design, and proposes a few SAR images segmentation algorithms based on multi-feature ensemble, which can be summarized as follows:
1. A context based hierarchical unequally merging for SAR image segmentation is proposed, which combines the top-down fashion and bottom-up fashion. Based on the Gestalt laws, three rules are proposed to represent superpixel context that realize a new and natural way to manage different kinds of features extracted from SAR images. The rules are prior knowledge from cognitive science and serve as top-down constraints to globally guide the superpixel merging. The features, including brightness, texture, edges, and spatial information, locally describe the superpixels of SAR images and are bottom-up forces. While merging superpixels, a hierarchical unequally merging algorithm is designed, which includes two stages:a) coarse merging stage; and b) fine merging stage. The merging algorithm unequally allocates computation resources so as to spend less running time in the superpixels without ambiguity and more running time in the superpixels with ambiguity. Experiments on synthetic and real SAR images indicate that this algorithm can make a balance between computation speed and segmentation accuracy.
2. A fuzzy clustering based on subspace iterative optimization is proposed, which is characterized by following aspects:1) multiple features have been extracted to accurately describe the objects in SAR images.2) A novel similarity measure using hierarchical ensemble is presented, which integrate these features of different properties respectively in the feature level and the similarity level to avoid the mutual influences between features and maximize discrimination ability of the similarity measure between objects.3) A novel spatial neighbourhood term with preferences has been introduced to the objective function, which can compute the contextual relationship between superpixels more accurately and reasonable than the traditional average methods.4) According to the objective function, a subspace iterative optimization algorithm is designed,which can optimize the objective function iteratively in each feature subspace to avoid the mutual influences between different kinds of features and preserve the distribution structures of the data points in every subspace.
3. Immune clonal selection optimization is carefully studied, and we propose two novel immune clonal selection optimization algorithms, which are:immunologic regulation clonal selection algorithm and clonal selection optimization based on orthogonal experiment design. Then, considering the advantages of the alternating minimization and the clonal selection optimization, we combine the two kinds of methods into a framework. The former one can iteratively search along the direction of the gradient steepest descent so as to accelerate the convergence speed of the algorithm; while the latter one can avoid premature convergence and find the global optimal solution with high probability so as to improve the robustness and accuracy of the algorithm.Experiments indicate that the proposed hybrid optimization method can achieve the best performance and is stable and effectiveness for various kinds of SAR images.
4. A context based unsupervised hierarchical iterative algorithm for SAR segmentation is proposed, which combines the advantages of the cluster based segmentation algorithms and region growing based segmentation algorithms.While merging superpixels,this algorithm chooses a hierarchical iterative strategy:a modified fuzzy c-means algorithm is first designed to analyze the appearance-based features of superpixels, and then region iterative growing is used to merge the similar superpixels based on contextual analysis in space domain.After that, a new loop of the clustering algorithm and the region growing begins. These two iterative sub-algorithms perform hierarchically and realize a natural and effective way to use different kinds of information to segment SAR images. Experiments on synthetic and real SAR images indicate that the proposed algorithm can obtain excellent segmentation and make a good balance between region consistency and preserving image details.
5. A two-stage algorithm for SAR image segmentation is presented by combing the advantages of the image-space based method and the feature-space based method. The algorithm consists of two stages:1) coarse merging stage, and2) fine classification stage. A context-based region iterative merging (CRIM) algorithm is proposed in the coarse merging stage, which can use many different kinds of information to guide the superpixel merging. The advantage of CRIM is that it can merge most of the superpixels inside the true segments at a very fast speed, so as to improve the accuracy of features and reduce the computation burden for the fine classification stage. Comparing with the traditional region merging algorithms, CRIM can take use of much more information, and a global halt index (HI) is carefully designed to decide the output of CRIM, which has global consistency in homogeneity. In the fine classification stage, the designed fuzzy clustering using hybrid optimization is adopted to produce the final segmentation result, which realizes the combination of the gradient-descent-direction search and the heuristic search. The former one can accelerate the algorithm's convergence speed; while the latter one can avoid premature and find the global optimal solution with high probability.
引文
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