基于Contourlet变换的SAR图像检索系统的研究与实现
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摘要
合成孔径雷达技术的发展导致了大量SAR图像的出现,这对SAR图像的处理及应用提出了新的需求。而基于文本关键词的传统检索方法已经不能适应图像检索的要求,使得基于内容的图像检索技术逐渐成为目前的研究热点。本文在全面分析基于内容的图像检索领域中的关键技术的基础上,结合江苏省自然科学基金,重点研究了多尺度几何分析技术Contourlet变换在基于图像纹理和形状特征的检索方法中的应用,并基于这些方法实现了SAR图像检索系统。主要工作概括如下:
1.系统分析和研究了基于内容的图像检索领域的一些关键技术,如:纹理、形状等图像低层特征的描述方法,图像间的相似性度量方法,图像库索引机制等。
2.研究了基于纹理的图像检索方法,并提出了一种基于Contourlet变换的纹理特征提取方法。通过对SAR图像及相关图像进行Contourlet分解,计算不同尺度不同方向上的系数幅度序列的均值和标准方差,以此构成特征向量来描述图像的纹理。
3.研究了基于形状的图像检索方法,并提出了一种基于Contourlet变换的形状特征提取方法。把Canny算子和Contourlet变换相结合,先对SAR图像及相关图像运用Canny算子提取边缘,在此基础上再进行Contourlet变换,把图像的形状信息分解到不同尺度不同方向上,从而保留各个频率分量,使图像形状信息的丢失减少。
4.用VC++6.0和Access2003设计了一个基于内容的SAR图像检索系统,把文中提出的基于Contourlet变换的纹理及形状提取方法应用到SAR图像检索中,实现了SAR图像的纹理及形状检索,并进行不同算法在同一平台下的效果对比,此外还实现了不同算法之间的组合,进一步改善了检索性能。
A large number of SAR images come forth because of the development of Synthetic Aperture Radar(SAR) technology. This results in a new demand of SAR image processing and its application. Traditional text keyword-based retrieval approach can't satisfy the demand of image retrieval, so content-based image retrieval (CBIR) technology becomes the current research focus. In this thesis, according to the natural science fund of Jiangsu Province, on the basis of analyzing the key techniques of CBIR, we mainly research on the application of the multiscale geometric analysis technology-the Contourlet transform in image retrieval algorithms based on texture feature and shape feature. In addition, these algorithms are applied to achieve a SAR image retrieval system. The main content of this thesis are summarized as follows:
1. Some key techniques and algorithms of CBIR, such as the low-level feature descriptions including texture, shape, the similarity measure between images, the indexing methods and so on, are deeply analyzed and studied .
2. We research on the texture-based image retrieval algorithm and propose an algorithm of texture feature extraction based on the Contourlet transform in this thesis. The image is decomposed by the Contourlet transform. The mean and standard deviation of the magnitude of the Contourlet coefficients at different scales and directions are computed to extract the texture feature vector.
3. We research on the shape-based image retrieval algorithm and propose an algorithm of shape feature extraction based on the Contourlet transform in this thesis. We combine the Canny operator and the Contourlet transform. On the basis of extracting the edge of the image with the Canny operator, Contourlet transform is used to decompose the shape information to different scales and different directions. As a result, shape information under each frequency is remained and loss is reduced.
4. We design a content-based SAR image retrieval system using Visual C++ 6.0 and Access2003. A SAR image retrieval system is carried out by using the texture and shape extraction algorithms based on the Contourlet transform. The system can not only implement the texture and shape retrieval of SAR images, but also be used for algorithm evaluation and performance comparison. Besides, different algorithms can be combined to receive better results.
1.系统分析和研究了基于内容的图像检索领域的一些关键技术,如:纹理、形状等图像低层特征的描述方法,图像间的相似性度量方法,图像库索引机制等。
2.研究了基于纹理的图像检索方法,并提出了一种基于Contourlet变换的纹理特征提取方法。通过对SAR图像及相关图像进行Contourlet分解,计算不同尺度不同方向上的系数幅度序列的均值和标准方差,以此构成特征向量来描述图像的纹理。
3.研究了基于形状的图像检索方法,并提出了一种基于Contourlet变换的形状特征提取方法。把Canny算子和Contourlet变换相结合,先对SAR图像及相关图像运用Canny算子提取边缘,在此基础上再进行Contourlet变换,把图像的形状信息分解到不同尺度不同方向上,从而保留各个频率分量,使图像形状信息的丢失减少。
4.用VC++6.0和Access2003设计了一个基于内容的SAR图像检索系统,把文中提出的基于Contourlet变换的纹理及形状提取方法应用到SAR图像检索中,实现了SAR图像的纹理及形状检索,并进行不同算法在同一平台下的效果对比,此外还实现了不同算法之间的组合,进一步改善了检索性能。
A large number of SAR images come forth because of the development of Synthetic Aperture Radar(SAR) technology. This results in a new demand of SAR image processing and its application. Traditional text keyword-based retrieval approach can't satisfy the demand of image retrieval, so content-based image retrieval (CBIR) technology becomes the current research focus. In this thesis, according to the natural science fund of Jiangsu Province, on the basis of analyzing the key techniques of CBIR, we mainly research on the application of the multiscale geometric analysis technology-the Contourlet transform in image retrieval algorithms based on texture feature and shape feature. In addition, these algorithms are applied to achieve a SAR image retrieval system. The main content of this thesis are summarized as follows:
1. Some key techniques and algorithms of CBIR, such as the low-level feature descriptions including texture, shape, the similarity measure between images, the indexing methods and so on, are deeply analyzed and studied .
2. We research on the texture-based image retrieval algorithm and propose an algorithm of texture feature extraction based on the Contourlet transform in this thesis. The image is decomposed by the Contourlet transform. The mean and standard deviation of the magnitude of the Contourlet coefficients at different scales and directions are computed to extract the texture feature vector.
3. We research on the shape-based image retrieval algorithm and propose an algorithm of shape feature extraction based on the Contourlet transform in this thesis. We combine the Canny operator and the Contourlet transform. On the basis of extracting the edge of the image with the Canny operator, Contourlet transform is used to decompose the shape information to different scales and different directions. As a result, shape information under each frequency is remained and loss is reduced.
4. We design a content-based SAR image retrieval system using Visual C++ 6.0 and Access2003. A SAR image retrieval system is carried out by using the texture and shape extraction algorithms based on the Contourlet transform. The system can not only implement the texture and shape retrieval of SAR images, but also be used for algorithm evaluation and performance comparison. Besides, different algorithms can be combined to receive better results.
引文
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[14] 章志勇,潘志庚,张明敏,基于多尺度通用傅里叶描述子的灰度图像检索,中国图像图形学报,2005,10(5):611～615。
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[17] Li Y.,Refarming the theory of invariant moments for pattern recognition,Pattern Recognition,1992,25(7):723～730。
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[20] 韦娜,耿国华,周明全,结合颜色和空间信息的图像检索算法,计算机应用与软件,2003,20(8):3～4。
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[26] A.Guttman,R-trees:a dynamic index structure for spatial searching,Proceedings of ACM SIGMOD international conference on Management of Data,1984:47～57。
[27] Shashi Shekhar,等,空间数据库,北京,机械工业出版社,2004:118～121。
[28] N.Beckmann,H.P.Kriegel,R.Schneider,et al.,The R*-Tree: An efficient and robust access method for points and rectangles,Proceedings of ACM SIGMOD international conference on Management of Data,1990: 322～331。
[29] 柳建平,岳丽华,赵振西,一种基于R树的图像检索方法,计算机工程与应用,2003,39(8):186～188。
[30] Y.Rui,T.S.Huang,S.Mehrotra,Content-based image retrieval with relevance feedbackin MARS,Proceedings of International Conference on Image Processing,Santa Barbara,1997:815～818。
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[32] 苏中,张宏江,马少平,基于贝叶斯分类器的图像检索相关反馈算法,软件学报,2002,13(10):2001～2006。
[33] 张磊,林福宗,张钹,基于前向神经网络的图像检索相关反馈算法设计,计算机学报,2002,25(07):673～680。
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