粒度理论下的多尺度遥感影像分割
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摘要
高空间分辨率遥感技术的发展在给遥感应用带来机遇的同时也带来了挑战,传统基于像素的影像分析方法局限性日益凸显。面向对象遥感影像分析技术在消除椒盐噪声、处理空间关系方面具有极大的优势,已成为当前发展趋势。在面向对象的遥感影像分析技术中,一个非常关键的技术就是影像分割。高分辨率遥感影像上,地物种类众多,且呈现层次性、结构化特征。为从多角度、多层次分析与理解地学现象,需要利用不同尺度的分割结果进行分析。但是,目前已有的大部分多尺度分割算法对高分辨率遥感影像的针对性不够,并且各层信息难以传递。鉴于此,论文将粒度理论里的粒度合成及粒度分层技术引入到高分辨率遥感影像分割过程中来,力图探索一条高分辨率遥感影像多尺度分割的有效途径。
论文从遥感影像所反映地物的多尺度特性出发,结合粒度合成及粒度分层技术,对粒度概念下的高空间分辨率遥感影像多尺度分割做出了一些理论探讨、算法设计和实验分析工作,主要内容如下：
(1)提出了一种嵌入边缘信息区域自适应的标记分水岭高分辨率遥感影像分割方法。该方法通过阈值分割方法从梯度影像上提取标记影像,且阈值通过区域自适应方法进行确定。对于每一像素,阈值由局部区域梯度分布及影像直方图的分位数共同决定。区域自适应的阈值生成方法保证了纹理丰富区域阈值高,而光谱变化平缓的区域阈值低。此方法生成的标记影像更符合实际情况。嵌入置信度的边缘提取方法一定程度上可提取弱边缘,且边缘定位精度高。利用其修改梯度影像,使得边缘上点最后被浸没到,成为对象边界。
(2)提出了两种标记分水岭多尺度影像分割方法：基于尺度空间和基于Mumford-Shah模型的多尺度影像分割方法。基于尺度空间的方法利用标记分水岭方法分割尺度空间中多个尺度的影像,从而获得多尺度的分割结果。同时以尺度空间为基础,建立多尺度分割与多尺度边缘提取间的尺度联系,将同一尺度影像上提取的边缘引入到标记分水岭分割过程中。基于Mumford-Shah模型的标记分水岭尺度化方法以标记分水岭方法所得同质斑块为基元,通过不断合并合并代价最低的相邻斑块来实现Mumford-Shah函数的最小化。以区域拟合误差与相邻斑块公共边长度的比值来定义合并代价,其间边缘信息的引入,使得公共边与边缘重叠度高的相邻斑块被合并概率降低。
(3)提出了基于分割结果评价的尺度选择方法。以分类样本为参考数据,利用分割结果评价方法评价各尺度分割结果的吻合程度,以选择各类地物的最佳分割尺度。分类样本反映了分类目的,因此,由此方法选择的尺度更能符合分类目。另外,遥感影像地物种类众多且固有尺度不尽相同,这也决定了需要选择出多个最合适分割尺度。考虑到同一地表覆盖类型内地物尺度相似,因此可以为每一种地表覆盖类型选择出一个最合适的分割尺度。对于某一地表覆盖类型,尺度选择时以该地表覆盖类型的样本数据为参考数据。
(4)提出了两种尺度综合理论与方法：基于粒度分层技术和基于粒度合成技术。基于粒度分层技术的尺度综合方法将影像分割过程分为两个层次：粗分割与细分割。粗分割将影像按地表覆盖类型划分为多个大的影像斑块,细分割则将每个大的斑块细分,细分所用尺度依据斑块所属地表覆盖类型确定。基于粒度合成的尺度综合方法利用尺度选择技术为每一地表覆盖类型选择一个最佳尺度的分割结果,形成中间粒度空间,并利用拓扑结构合成技术完成粒度空间的合成,最终实现尺度综合。
论文的主要创新点在于：
(1)提出了两种标记分水岭多尺度分割方法：基于尺度空间的以及基于Mumford-Shah式的。基于尺度空间的标记分水岭多尺度分割方法以尺度空间为基础,建立了多尺度边缘提取与多尺度影像分割间的尺度联系,每个尺度影像分割时引入对应尺度的边缘信息。基于Mumford-Shah式的标记分水岭多尺度分割方法,以标记分水岭方法所得同质斑块为基元并引入边缘信息,可消除大量小面积斑块及提高边界定位精度。
(2)提出了基于分割结果评价的尺度选择技术。以分割评价结果为标准进行尺度选择,而分割质量体现了分割结果与样本数据的符合程度,因此该尺度选择方法所得尺度能更好的符合应用目的。
(3)提出了两种尺度综合理论和方法：基于粒度分层理论及基于粒度合成理论。基于粒度分层的尺度综合方法利用分层方法来分割遥感影像,利用上一层获得的信息指导下一层分割尺度的选择,实现了信息在不同层之间的传递,同时实现了多个尺度分割结果的综合。基于粒度合成的尺度综合方法将不同尺度的分割结果综合,最终分割结果中综合了多个尺度的影像斑块。该方法将多维任务分解为多个一维任务然后进行综合,提高分割效率和减小分割难度。
本文将粒度概念引入到高空间分辨率遥感影像分割过程中,实验与分析表明,本文所提方法能自适应的为包含不同类型地物的影像、不同分类目的提供满足分类要求的分割结果,具有良好的可行性与有效性。
尚需进一步研究的问题：
(1)研究能依据地表覆盖类型自动确定尺度选择中的分割结果度量标准与方法,增强尺度选择技术的自适应性。
(2)研究不同地物类型对应分割尺度之间的差异,将地物划分为更多地表覆盖类型,使得同一地表覆盖类型中的地物分割尺度更为接近。
(3)在基于粒度分层技术的尺度综合里,充分利用上一层分割中获得信息(如斑块的破碎度)进行尺度选择,提高分割效率。
(4)研究每种地表覆盖类型最佳分割特征与分割方法,并利用不同特征与方法来获取每种地表覆盖类型的最适合的分割结果。
High spatial resolution remote sensing image (HSRI) provides both the opportunity and challenge for remote sensing application. With the spatial resolution refinement, the limitation of the traditional pixel-based image analysis method becomes obvious. Object-based image analysis technique can eliminate the'salt and pepper' effect and is quite efficient in using spatial or contextual information. It thus becomes the first choice for HSRI application recently. As a fundamental process, HSRI segmentation partitions the images into un-overlapping homogenous regions or objects. The segmentation quality has a direct influence on the latter image analysis. In HSRI, the various landscapes patterns exhibit multi-scale hierarchical and structural characteristics, which change depending on the scale of observation. Consequently, there often does not exist a single scale of segmentation that could be deemed appropriate for analysis of the entire image. Clearly, a multi-scale analysis of the image is necessary which naturally entails a segmentation technique that is capable of generating a multi-scale representation of the image data. Till now, a lot of multi-scale segmentation algorithms have been developed. However, most of them are not aiming at HSRI segmentation and have difficulty in multi-scale information transferring. In view of these limitations, this dissertation introduces the granularity synthesis and granularity stratification techniques into the segmentation process to explore an effective multi-scale segmentation approach for HSRI based on the granular theory.
The dissertation starts from the multi-scale characteristics of gournd objects on HSRI, proposes a new approach for multi-scale image segmentation based on the gradular theory. Both the granularity stratification and granularity synthesis techniques are implemented in the synthesization process of multi-scale segmentation results. The involved key theories, processing algorithms and applications are researched and the major works are listed as follows:
1. A regional-adaptive marker-based watershed algorithm integrating edge information is developed. The marker image is firstly extracted by a regional-adaptive threshold segmentation of the gradient image to solve the over-segmentation problem. Instead of using a fixed single threshold, a threshold image is firstly estimated. For each pixel, the threshold value is determined by the gradient distribution of the local region and the fractile value of the image histogram. As a result, the threshold values of the textured regions are relatively high and the threshold values of the spectral homogenous regions are relatively low. The extracted markers are more coincide with the inner regions of the ground objects. Then, to retain the weak object boundaries and improve the boundary location accuracy, the edge detected by the confidence-embedded method is integrated into the proposed algorithm. Both the marker image and the gradient image are rectified according to the edge information to ensure the edge pixels are labeled lastly as the object boundary pixels.
2. Two multi-scale segmentation methods are developed based on the scale space theory and the Mumford-Shah model respectively. In the first method, the image is transformed into multi-scale images by nonlinear filters to construct a scale space firstly. Then, the multi-scale images are segmented by the proposed marker-based watershed algorithm to achieve multi-scale segmentation results. Based on the scale space theory, image segmentation and edge detection can be connected naturally. For each image scale, the detected edge is integrated into the segmentation process to achieve the corresponding scale of segmentation result. In the Mumford-Shah model method, the initial homogenous elements are extracted by the proposed watershed algorithm at the beginning. Then the neighboring homogenous elements with the lowest merging prices are merged with the Mumford-Shah function value minimized. Each merging operation generates a single scale of segmentation. After many times of object merging, a series of multi-scale image segmentation results can be achieved. Here, the merging price is defined as the ratio of the region fitting error and the neighboring objects' common boundary length. In this method, the edge information can be integrated by reducing the merging probability of the common boundary that has high overlapping rate with the edge information.
3. Scale selection based on supervised segmentation quality evaluation is studied. Classification samples are used as the reference data for segmentation quality assessment to find the optimal segmentation scale. Considering that the samples reflect the classification target, the selected scale will better fit the application requirements. Furthermore, because the objects appear with different inherent scale, it is necessary to select multiple optimal segmentation scales for different objects. With consideration that objects belonging to the same land cover class are usually with the same optimal scale, an optimal segmentation scale can be determined for each land cover class using the corresponding samples.
4. The scale synthesis theory and methods are studied under the concept of granular theory. Both the granularity stratification and granularity synthesis techniques are researched and implemented for scale synthesis. In the granularity stratification method, the image segmentation is divided into two levels:coarse segmentation and fine-grained segmentation. The coarse segmentation partitions the image into multiple large regions according to the land cover types in advance. Then, each region is segmented with the optimal segmentation scale determined by the land cover type in the fine-grained segmentation. In the granularity synthesis method, the optimal segmentation result for each land cover type is selected as the medium granular space, then these granularity spaces are combined to realize the scale synthesis.
The main innovative points are as follows:
1. A regional adaptive marker-based watershed segmentation method integrating edge information is proposed. Based on the scale space theory and the Mumford-Shah model, two multi-scale image segmentation algorithms are proposed. With the corresponding edge information integrated, these methods are capable in eliminating the small undesired objects and retaining the weak object boundaries in the final segmentation result. Moreover, the proposed methods are of high efficiency for large image data.
2. The scale selection technique based on the supervised segmentation evaluation is proposed. The classification samples are used as reference data, and the discrepancy measures are used to evaluate the segmentation result. Because the segmentation assessment result reflects the similarity between segmentation result and the classification samples, the selected scale can fit the application requirement well.
3. The granularity theory is studied and two kinds of scale synthesis methods are developed using the granularity stratification and granularity synthesis techniques. The granularity stratification method can transfer the information of the coarse level segmentation to the fine-grained level of segmentations. The information of the coarse leve segmentation is used to direct the selection of the finer segmentation scales. The optimal scales of segmentation results are synthesized to achieve the final segmentation result. The granularity synthesis method combines different scale of segmentation results to achieve the segmentation result. It can simplify the image segmentation multi-dimensional tasks into multiple single dimensional tasks and get the final result by synthesis methods.
This dissertation introduces the granular theory into the segmentation of HSRI. Experiments show the proposed methods can adaptively produce good segmentation results for images with different land cover types and meet the segmentation requirements of different classification purposes.
Further research is needed on the following issues:
1. To enhance the self-adaptiveness of the scale selection techniques, segmentation assessment measures should be further studied to standardize the segmentation assessment procedures.
2. The difference of segmentation scales among different gound objects should be further studied. The image can be divided into more land cover types to ensure that the optimal segmentation scale of each land cover type is nearly the same.
3. In the granularity stratification method, further research on how to use the different information of the coarser level (such as the degree of fragmentation) is needed.
4. The optimal features and methods to be used for segmentation of different ground objects should be further studied.
论文从遥感影像所反映地物的多尺度特性出发,结合粒度合成及粒度分层技术,对粒度概念下的高空间分辨率遥感影像多尺度分割做出了一些理论探讨、算法设计和实验分析工作,主要内容如下：
(1)提出了一种嵌入边缘信息区域自适应的标记分水岭高分辨率遥感影像分割方法。该方法通过阈值分割方法从梯度影像上提取标记影像,且阈值通过区域自适应方法进行确定。对于每一像素,阈值由局部区域梯度分布及影像直方图的分位数共同决定。区域自适应的阈值生成方法保证了纹理丰富区域阈值高,而光谱变化平缓的区域阈值低。此方法生成的标记影像更符合实际情况。嵌入置信度的边缘提取方法一定程度上可提取弱边缘,且边缘定位精度高。利用其修改梯度影像,使得边缘上点最后被浸没到,成为对象边界。
(2)提出了两种标记分水岭多尺度影像分割方法：基于尺度空间和基于Mumford-Shah模型的多尺度影像分割方法。基于尺度空间的方法利用标记分水岭方法分割尺度空间中多个尺度的影像,从而获得多尺度的分割结果。同时以尺度空间为基础,建立多尺度分割与多尺度边缘提取间的尺度联系,将同一尺度影像上提取的边缘引入到标记分水岭分割过程中。基于Mumford-Shah模型的标记分水岭尺度化方法以标记分水岭方法所得同质斑块为基元,通过不断合并合并代价最低的相邻斑块来实现Mumford-Shah函数的最小化。以区域拟合误差与相邻斑块公共边长度的比值来定义合并代价,其间边缘信息的引入,使得公共边与边缘重叠度高的相邻斑块被合并概率降低。
(3)提出了基于分割结果评价的尺度选择方法。以分类样本为参考数据,利用分割结果评价方法评价各尺度分割结果的吻合程度,以选择各类地物的最佳分割尺度。分类样本反映了分类目的,因此,由此方法选择的尺度更能符合分类目。另外,遥感影像地物种类众多且固有尺度不尽相同,这也决定了需要选择出多个最合适分割尺度。考虑到同一地表覆盖类型内地物尺度相似,因此可以为每一种地表覆盖类型选择出一个最合适的分割尺度。对于某一地表覆盖类型,尺度选择时以该地表覆盖类型的样本数据为参考数据。
(4)提出了两种尺度综合理论与方法：基于粒度分层技术和基于粒度合成技术。基于粒度分层技术的尺度综合方法将影像分割过程分为两个层次：粗分割与细分割。粗分割将影像按地表覆盖类型划分为多个大的影像斑块,细分割则将每个大的斑块细分,细分所用尺度依据斑块所属地表覆盖类型确定。基于粒度合成的尺度综合方法利用尺度选择技术为每一地表覆盖类型选择一个最佳尺度的分割结果,形成中间粒度空间,并利用拓扑结构合成技术完成粒度空间的合成,最终实现尺度综合。
论文的主要创新点在于：
(1)提出了两种标记分水岭多尺度分割方法：基于尺度空间的以及基于Mumford-Shah式的。基于尺度空间的标记分水岭多尺度分割方法以尺度空间为基础,建立了多尺度边缘提取与多尺度影像分割间的尺度联系,每个尺度影像分割时引入对应尺度的边缘信息。基于Mumford-Shah式的标记分水岭多尺度分割方法,以标记分水岭方法所得同质斑块为基元并引入边缘信息,可消除大量小面积斑块及提高边界定位精度。
(2)提出了基于分割结果评价的尺度选择技术。以分割评价结果为标准进行尺度选择,而分割质量体现了分割结果与样本数据的符合程度,因此该尺度选择方法所得尺度能更好的符合应用目的。
(3)提出了两种尺度综合理论和方法：基于粒度分层理论及基于粒度合成理论。基于粒度分层的尺度综合方法利用分层方法来分割遥感影像,利用上一层获得的信息指导下一层分割尺度的选择,实现了信息在不同层之间的传递,同时实现了多个尺度分割结果的综合。基于粒度合成的尺度综合方法将不同尺度的分割结果综合,最终分割结果中综合了多个尺度的影像斑块。该方法将多维任务分解为多个一维任务然后进行综合,提高分割效率和减小分割难度。
本文将粒度概念引入到高空间分辨率遥感影像分割过程中,实验与分析表明,本文所提方法能自适应的为包含不同类型地物的影像、不同分类目的提供满足分类要求的分割结果,具有良好的可行性与有效性。
尚需进一步研究的问题：
(1)研究能依据地表覆盖类型自动确定尺度选择中的分割结果度量标准与方法,增强尺度选择技术的自适应性。
(2)研究不同地物类型对应分割尺度之间的差异,将地物划分为更多地表覆盖类型,使得同一地表覆盖类型中的地物分割尺度更为接近。
(3)在基于粒度分层技术的尺度综合里,充分利用上一层分割中获得信息(如斑块的破碎度)进行尺度选择,提高分割效率。
(4)研究每种地表覆盖类型最佳分割特征与分割方法,并利用不同特征与方法来获取每种地表覆盖类型的最适合的分割结果。
High spatial resolution remote sensing image (HSRI) provides both the opportunity and challenge for remote sensing application. With the spatial resolution refinement, the limitation of the traditional pixel-based image analysis method becomes obvious. Object-based image analysis technique can eliminate the'salt and pepper' effect and is quite efficient in using spatial or contextual information. It thus becomes the first choice for HSRI application recently. As a fundamental process, HSRI segmentation partitions the images into un-overlapping homogenous regions or objects. The segmentation quality has a direct influence on the latter image analysis. In HSRI, the various landscapes patterns exhibit multi-scale hierarchical and structural characteristics, which change depending on the scale of observation. Consequently, there often does not exist a single scale of segmentation that could be deemed appropriate for analysis of the entire image. Clearly, a multi-scale analysis of the image is necessary which naturally entails a segmentation technique that is capable of generating a multi-scale representation of the image data. Till now, a lot of multi-scale segmentation algorithms have been developed. However, most of them are not aiming at HSRI segmentation and have difficulty in multi-scale information transferring. In view of these limitations, this dissertation introduces the granularity synthesis and granularity stratification techniques into the segmentation process to explore an effective multi-scale segmentation approach for HSRI based on the granular theory.
The dissertation starts from the multi-scale characteristics of gournd objects on HSRI, proposes a new approach for multi-scale image segmentation based on the gradular theory. Both the granularity stratification and granularity synthesis techniques are implemented in the synthesization process of multi-scale segmentation results. The involved key theories, processing algorithms and applications are researched and the major works are listed as follows:
1. A regional-adaptive marker-based watershed algorithm integrating edge information is developed. The marker image is firstly extracted by a regional-adaptive threshold segmentation of the gradient image to solve the over-segmentation problem. Instead of using a fixed single threshold, a threshold image is firstly estimated. For each pixel, the threshold value is determined by the gradient distribution of the local region and the fractile value of the image histogram. As a result, the threshold values of the textured regions are relatively high and the threshold values of the spectral homogenous regions are relatively low. The extracted markers are more coincide with the inner regions of the ground objects. Then, to retain the weak object boundaries and improve the boundary location accuracy, the edge detected by the confidence-embedded method is integrated into the proposed algorithm. Both the marker image and the gradient image are rectified according to the edge information to ensure the edge pixels are labeled lastly as the object boundary pixels.
2. Two multi-scale segmentation methods are developed based on the scale space theory and the Mumford-Shah model respectively. In the first method, the image is transformed into multi-scale images by nonlinear filters to construct a scale space firstly. Then, the multi-scale images are segmented by the proposed marker-based watershed algorithm to achieve multi-scale segmentation results. Based on the scale space theory, image segmentation and edge detection can be connected naturally. For each image scale, the detected edge is integrated into the segmentation process to achieve the corresponding scale of segmentation result. In the Mumford-Shah model method, the initial homogenous elements are extracted by the proposed watershed algorithm at the beginning. Then the neighboring homogenous elements with the lowest merging prices are merged with the Mumford-Shah function value minimized. Each merging operation generates a single scale of segmentation. After many times of object merging, a series of multi-scale image segmentation results can be achieved. Here, the merging price is defined as the ratio of the region fitting error and the neighboring objects' common boundary length. In this method, the edge information can be integrated by reducing the merging probability of the common boundary that has high overlapping rate with the edge information.
3. Scale selection based on supervised segmentation quality evaluation is studied. Classification samples are used as the reference data for segmentation quality assessment to find the optimal segmentation scale. Considering that the samples reflect the classification target, the selected scale will better fit the application requirements. Furthermore, because the objects appear with different inherent scale, it is necessary to select multiple optimal segmentation scales for different objects. With consideration that objects belonging to the same land cover class are usually with the same optimal scale, an optimal segmentation scale can be determined for each land cover class using the corresponding samples.
4. The scale synthesis theory and methods are studied under the concept of granular theory. Both the granularity stratification and granularity synthesis techniques are researched and implemented for scale synthesis. In the granularity stratification method, the image segmentation is divided into two levels:coarse segmentation and fine-grained segmentation. The coarse segmentation partitions the image into multiple large regions according to the land cover types in advance. Then, each region is segmented with the optimal segmentation scale determined by the land cover type in the fine-grained segmentation. In the granularity synthesis method, the optimal segmentation result for each land cover type is selected as the medium granular space, then these granularity spaces are combined to realize the scale synthesis.
The main innovative points are as follows:
1. A regional adaptive marker-based watershed segmentation method integrating edge information is proposed. Based on the scale space theory and the Mumford-Shah model, two multi-scale image segmentation algorithms are proposed. With the corresponding edge information integrated, these methods are capable in eliminating the small undesired objects and retaining the weak object boundaries in the final segmentation result. Moreover, the proposed methods are of high efficiency for large image data.
2. The scale selection technique based on the supervised segmentation evaluation is proposed. The classification samples are used as reference data, and the discrepancy measures are used to evaluate the segmentation result. Because the segmentation assessment result reflects the similarity between segmentation result and the classification samples, the selected scale can fit the application requirement well.
3. The granularity theory is studied and two kinds of scale synthesis methods are developed using the granularity stratification and granularity synthesis techniques. The granularity stratification method can transfer the information of the coarse level segmentation to the fine-grained level of segmentations. The information of the coarse leve segmentation is used to direct the selection of the finer segmentation scales. The optimal scales of segmentation results are synthesized to achieve the final segmentation result. The granularity synthesis method combines different scale of segmentation results to achieve the segmentation result. It can simplify the image segmentation multi-dimensional tasks into multiple single dimensional tasks and get the final result by synthesis methods.
This dissertation introduces the granular theory into the segmentation of HSRI. Experiments show the proposed methods can adaptively produce good segmentation results for images with different land cover types and meet the segmentation requirements of different classification purposes.
Further research is needed on the following issues:
1. To enhance the self-adaptiveness of the scale selection techniques, segmentation assessment measures should be further studied to standardize the segmentation assessment procedures.
2. The difference of segmentation scales among different gound objects should be further studied. The image can be divided into more land cover types to ensure that the optimal segmentation scale of each land cover type is nearly the same.
3. In the granularity stratification method, further research on how to use the different information of the coarser level (such as the degree of fragmentation) is needed.
4. The optimal features and methods to be used for segmentation of different ground objects should be further studied.
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