基于非模糊均值漂移的高空间分辨率遥感影像区域分割算法研究
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摘要
航空航天技术、数据自动采集和传输技术、数据压缩技术和数据库技术的快速发展,使得大量的遥感数据积累下来。但是,这些数据的处理远远不能满足实际应用的需求。遥感数据的应用水平严重的滞后于空间遥感技术的发展。高空间分辨率影像的出现为遥感技术的发展带来了新的机遇,增加了传统遥感应用的深度。发展智能化的遥感影像数据分析和理解技术,利用计算机模拟人脑对遥感影像解译的认知过程,依据应用目的来获得影像内容的语义信息是高分辨率遥感影像解译的重要任务。而这些技术都需要建立在遥感影像区域分割算法基础上。
在从像素空间经特征空间到语义空间的高分辨率遥感影像解译过程中,区域分割是重要一环,有许多问题值得研究。非模糊均值漂移算法作为一种非参数概率密度估计算法,具有良好的理论基础,且适合并行运算,适用于各种特征空间分析的场合。本文采用非模糊均值漂移算法为工具,就高空间分辨率影像的分割问题展开研究。相关的工作可概括为以下几点：
1.非模糊均值漂移算法是一种统计迭代算法,算法的收敛性是其应用的理论基础。然而,相关经典文献的收敛性证明却存在错误和不完善之处。本研究梳理了核函数与剖面函数、核函数与阴影核函数的关系,然后基于剖面函数和阴影核的性质严格证明了概率密度估计序列的收敛性,在此基础上又给出了采用带宽矩阵、单一变带宽、变权重不同形式的非模糊均值漂移算法的收敛性证明。
2.针对高空间分辨率遥感影像的特点,给出了光谱和纹理信息融合的像素级和区域级分割算法。两种算法中,均采用Gabor滤波器组的卷积输出作为纹理特征,并基于特征的方差设计了加权最小距离分类器。为获得像素级的光谱特征,提出了一种均值漂移滤波算法的自适应带宽确定方法,并采用该算法对各像素灰度值组成的特征空间进行滤波,将滤波结果作为光谱特征。对于区域级分割,将均值漂移分割算法获得的区域模态的光谱分量作为光谱特征。实验结果表明：提出的均值漂移带宽确定方法是有效的；加权融合算法较基于光谱的分割方法在分割精度上有一定程度的提高；区域级的分类方法无论是客观精度还是主观评价都要优于像素级的结果。
3.考虑到传统均值漂移算法带宽参数不易控制,分割结果缺乏稳定性的缺点,提出了一种小波域的遥感影像多尺度均值漂移分割算法。算法通过小波变换将不同分辨率的分割结果融合起来,采用形态学方法实现分割区域边界优化。航空遥感影像和合成影像的实验表明：该算法分割精度要优于4种对比算法,且具有参数调整简单和时间复杂度低的优点。
4.地表景观本身具有多层次的组织结构,不同的地物需要适当的比例尺,才能完整的观测。论文研究了和地表景观的语义层次对应的层次分割区域树的构建方法,采用边缘置信度加权的均值漂移分割算法作为初始分割,构建了一个两步式的多层次分割框架。在该框架下,首先实现了一种油罐目标提取和油库定位算法。油罐识别问题可视为同一语义层次下的地物、空间尺度不一致时的分割问题的一个实例。相关实验证实了算法的有效性。影像的大尺度划分可为影像进一步的分析打下基础。考虑到高分辨率遥感影像在大尺度上表现出较强的纹理性,在提出的多尺度框架下,还实现了一种基于区域纹理建模的、逐步合并的多层次分割算法。算法采用区域的局部二值模式特征和K均值聚类算法获得的影像标记构建联合直方图,对区域进行建模,采用G统计量度量直方图相似性。采用合成影像,和MRF纹理分割方法等的对比证实了算法的有效性。最后,提出了一种基于多层次分割的特征自适应提取算法,并用于城区地物覆盖分类。算法将最优的分割结果和地物对象联系起来,对同一地物的不同尺度提取特征。两组高光谱实验数据的结果表明：在采用相同的训练和测试数据及分类器的情况下,对比相关文献的结果,本文提出的算法无论是分类精度还是视觉评价都获得了较好的结果,从而验证了特征提取算法的有效性。三种具体的分割应用验证了两步式的多尺度分割框架的可行性。
A large number of remote sensing data has been accumulated with the rapid development of several technologies, such as the aerospace technology, the automatic data acquisition and transmission technology, the data compression technology and the database technology, et al. However, the processing of these data is far from satisfying the needs of practical applications. The application of remote sensing data lags far behind the development of space technology. The appearance of high spatial resolution remote sensing images has brought new opportunities for the development of remote sensing technology and increased the depth of traditional remote sensing applications. The development of intelligent remote sensing image data analysis and understanding techniques, the simulation of remote sensing image interpretation of the human brain's cognitive processes by computer, getting the semantic information from high-resolution images based on the purpose of practical applications are important tasks of image interpretation. And all of these techniques are built on region-based image segmentation algorithms.
During image interpretation process from the pixel space through the feature space to semantic space, the region segmentation plays an important part; there are many issues worth studying. As a non-parametric probability density estimation algorithm, the nonblurring mean shift algorithm which can be used for feature space analysis of a variety of occasions has a good theoretical basis and is suitable for parallel computing. In my work, the nonBlurring mean shift algorithm is used as a mathematic tool for the research of high spatial resolution image segmentation. The work can be summarized as the following:
1. The nonBlurring mean shift algorithm is a statistical iterative algorithm; the convergence of the algorithm is the theoretical basis for its application. However, there are some mistakes and imperfections in the proof procedure of its convergence in some classical literatures. The research firstly emphasized the relation between kernel function and profile function, kernel function and the shadow of kernel function. Secondly, the convergence of the probability density estimation sequence is proved strictly according to properties of the profile function and the shadow of the kernel function. Finally, the convergence of nonBlurring mean shift procedures with different forms, such as the bandwidth matrix, the variable bandwidth, the variable weight, is discussed.
2. A pixel-level and a region-level segmentation algorithm based on spectral and texture information fusion are presented for high spatial resolution remote sensing images. Gabor filter banks are used to extract texture features and a weighted minimum distance classifier is designed on feature variances in both methods. In order to obtain pixel-level spectral features, the paper presents an adaptive bandwidth mean-shift filtering algorithm using pixel numbers as input and using output as selected spectral features, which determines bandwidth based on Gauss assumption. The spectral part of the region mode is used as the region-level spectral feature. The experiments show that:the proposed adaptive mean-shift filtering algorithm is effective; the weighted fusion algorithm has higher segmentation accuracy than methods only using spectral features; the region-level method is superior to the pixel level method in both the objective and subjective evaluation
3. Considering the bandwidths of the traditional mean-shift algorithm is not easy to control and the lack of stability of the segmentation results, a wavelet domain multiscale mean-shift segmentation algorithm is presented. The algorithm integrates the segmentation result in different wavelet resolutions by wavelet transform and the region boundary in the finest scale is optimized by morphological operation. Airborne image and synthetic image are used for validating the algorithm. Experiments show that:the segmentation algorithm is superior to the compared four algorithms, has a simple parameter setting and low time complexity.
4.The landscape has a multi-level hierarchical structure, land objects only can be observed completely in a appropriate scale range. The methodology that constructing a multilevel hierarchical region structure, which is associated the multi-level hierarchical structure of the real word is studied, and a two-step framework for multi-level segmentation is built. Under this framework, an oil tank extraction and oil depots location algorithm is presented firstly and validated by experiments. Tank extraction in high resolution images can be treated as an example of the segmentation problems from the same semantic level to a different image spatial scale. The large-scale region partition of images will benefit further analysis. Since the large-scale high resolution remote sensing images have demonstrated a strong texture property, a multi-level segmentation algorithm based on region texture modeling and stepwise merging is presented. The method models the region with the joint histogram of local binary pattern and the labels of K means algorithm, and G-statistic is used to measure the difference of two histograms. Compared with MRF based segmentation methods using synthetic images, the method's accuracy has been validated. Finally, a multi-level segmentation procedure for adaptive feature extraction algorithm is presented and used for urban land cover classification. The method associates meaningful segments with land objects and features are extracted from the different size scale of the same land object. Two hyperspectral data sets are used for validating the method. Compared with other methods using the same training set, testing set and classifier, our results have show superior results in both visual evaluation and classification accuracy.
在从像素空间经特征空间到语义空间的高分辨率遥感影像解译过程中,区域分割是重要一环,有许多问题值得研究。非模糊均值漂移算法作为一种非参数概率密度估计算法,具有良好的理论基础,且适合并行运算,适用于各种特征空间分析的场合。本文采用非模糊均值漂移算法为工具,就高空间分辨率影像的分割问题展开研究。相关的工作可概括为以下几点：
1.非模糊均值漂移算法是一种统计迭代算法,算法的收敛性是其应用的理论基础。然而,相关经典文献的收敛性证明却存在错误和不完善之处。本研究梳理了核函数与剖面函数、核函数与阴影核函数的关系,然后基于剖面函数和阴影核的性质严格证明了概率密度估计序列的收敛性,在此基础上又给出了采用带宽矩阵、单一变带宽、变权重不同形式的非模糊均值漂移算法的收敛性证明。
2.针对高空间分辨率遥感影像的特点,给出了光谱和纹理信息融合的像素级和区域级分割算法。两种算法中,均采用Gabor滤波器组的卷积输出作为纹理特征,并基于特征的方差设计了加权最小距离分类器。为获得像素级的光谱特征,提出了一种均值漂移滤波算法的自适应带宽确定方法,并采用该算法对各像素灰度值组成的特征空间进行滤波,将滤波结果作为光谱特征。对于区域级分割,将均值漂移分割算法获得的区域模态的光谱分量作为光谱特征。实验结果表明：提出的均值漂移带宽确定方法是有效的；加权融合算法较基于光谱的分割方法在分割精度上有一定程度的提高；区域级的分类方法无论是客观精度还是主观评价都要优于像素级的结果。
3.考虑到传统均值漂移算法带宽参数不易控制,分割结果缺乏稳定性的缺点,提出了一种小波域的遥感影像多尺度均值漂移分割算法。算法通过小波变换将不同分辨率的分割结果融合起来,采用形态学方法实现分割区域边界优化。航空遥感影像和合成影像的实验表明：该算法分割精度要优于4种对比算法,且具有参数调整简单和时间复杂度低的优点。
4.地表景观本身具有多层次的组织结构,不同的地物需要适当的比例尺,才能完整的观测。论文研究了和地表景观的语义层次对应的层次分割区域树的构建方法,采用边缘置信度加权的均值漂移分割算法作为初始分割,构建了一个两步式的多层次分割框架。在该框架下,首先实现了一种油罐目标提取和油库定位算法。油罐识别问题可视为同一语义层次下的地物、空间尺度不一致时的分割问题的一个实例。相关实验证实了算法的有效性。影像的大尺度划分可为影像进一步的分析打下基础。考虑到高分辨率遥感影像在大尺度上表现出较强的纹理性,在提出的多尺度框架下,还实现了一种基于区域纹理建模的、逐步合并的多层次分割算法。算法采用区域的局部二值模式特征和K均值聚类算法获得的影像标记构建联合直方图,对区域进行建模,采用G统计量度量直方图相似性。采用合成影像,和MRF纹理分割方法等的对比证实了算法的有效性。最后,提出了一种基于多层次分割的特征自适应提取算法,并用于城区地物覆盖分类。算法将最优的分割结果和地物对象联系起来,对同一地物的不同尺度提取特征。两组高光谱实验数据的结果表明：在采用相同的训练和测试数据及分类器的情况下,对比相关文献的结果,本文提出的算法无论是分类精度还是视觉评价都获得了较好的结果,从而验证了特征提取算法的有效性。三种具体的分割应用验证了两步式的多尺度分割框架的可行性。
A large number of remote sensing data has been accumulated with the rapid development of several technologies, such as the aerospace technology, the automatic data acquisition and transmission technology, the data compression technology and the database technology, et al. However, the processing of these data is far from satisfying the needs of practical applications. The application of remote sensing data lags far behind the development of space technology. The appearance of high spatial resolution remote sensing images has brought new opportunities for the development of remote sensing technology and increased the depth of traditional remote sensing applications. The development of intelligent remote sensing image data analysis and understanding techniques, the simulation of remote sensing image interpretation of the human brain's cognitive processes by computer, getting the semantic information from high-resolution images based on the purpose of practical applications are important tasks of image interpretation. And all of these techniques are built on region-based image segmentation algorithms.
During image interpretation process from the pixel space through the feature space to semantic space, the region segmentation plays an important part; there are many issues worth studying. As a non-parametric probability density estimation algorithm, the nonblurring mean shift algorithm which can be used for feature space analysis of a variety of occasions has a good theoretical basis and is suitable for parallel computing. In my work, the nonBlurring mean shift algorithm is used as a mathematic tool for the research of high spatial resolution image segmentation. The work can be summarized as the following:
1. The nonBlurring mean shift algorithm is a statistical iterative algorithm; the convergence of the algorithm is the theoretical basis for its application. However, there are some mistakes and imperfections in the proof procedure of its convergence in some classical literatures. The research firstly emphasized the relation between kernel function and profile function, kernel function and the shadow of kernel function. Secondly, the convergence of the probability density estimation sequence is proved strictly according to properties of the profile function and the shadow of the kernel function. Finally, the convergence of nonBlurring mean shift procedures with different forms, such as the bandwidth matrix, the variable bandwidth, the variable weight, is discussed.
2. A pixel-level and a region-level segmentation algorithm based on spectral and texture information fusion are presented for high spatial resolution remote sensing images. Gabor filter banks are used to extract texture features and a weighted minimum distance classifier is designed on feature variances in both methods. In order to obtain pixel-level spectral features, the paper presents an adaptive bandwidth mean-shift filtering algorithm using pixel numbers as input and using output as selected spectral features, which determines bandwidth based on Gauss assumption. The spectral part of the region mode is used as the region-level spectral feature. The experiments show that:the proposed adaptive mean-shift filtering algorithm is effective; the weighted fusion algorithm has higher segmentation accuracy than methods only using spectral features; the region-level method is superior to the pixel level method in both the objective and subjective evaluation
3. Considering the bandwidths of the traditional mean-shift algorithm is not easy to control and the lack of stability of the segmentation results, a wavelet domain multiscale mean-shift segmentation algorithm is presented. The algorithm integrates the segmentation result in different wavelet resolutions by wavelet transform and the region boundary in the finest scale is optimized by morphological operation. Airborne image and synthetic image are used for validating the algorithm. Experiments show that:the segmentation algorithm is superior to the compared four algorithms, has a simple parameter setting and low time complexity.
4.The landscape has a multi-level hierarchical structure, land objects only can be observed completely in a appropriate scale range. The methodology that constructing a multilevel hierarchical region structure, which is associated the multi-level hierarchical structure of the real word is studied, and a two-step framework for multi-level segmentation is built. Under this framework, an oil tank extraction and oil depots location algorithm is presented firstly and validated by experiments. Tank extraction in high resolution images can be treated as an example of the segmentation problems from the same semantic level to a different image spatial scale. The large-scale region partition of images will benefit further analysis. Since the large-scale high resolution remote sensing images have demonstrated a strong texture property, a multi-level segmentation algorithm based on region texture modeling and stepwise merging is presented. The method models the region with the joint histogram of local binary pattern and the labels of K means algorithm, and G-statistic is used to measure the difference of two histograms. Compared with MRF based segmentation methods using synthetic images, the method's accuracy has been validated. Finally, a multi-level segmentation procedure for adaptive feature extraction algorithm is presented and used for urban land cover classification. The method associates meaningful segments with land objects and features are extracted from the different size scale of the same land object. Two hyperspectral data sets are used for validating the method. Compared with other methods using the same training set, testing set and classifier, our results have show superior results in both visual evaluation and classification accuracy.
引文
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