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1、上海交通大学硕士学位论文多光谱遥感图像的特征提取与比较姓名：刘磊申请学位级别：硕士专业：控制理论与控制工程指导教师：敬忠良20050101I多光谱遥感图像的特征提取与比较 摘 要 基于内容的图像检索方式 CBIR(Content-Based Image Retrieval) 就是根据给定的图像特征从存储在数据库中的大量图像中进行检索找出与给定图像特征相似的图像来基于内容的图像检索主要涉及到四项关键技术图像数据库技术内容描述技术特征提取与匹配技术快速检索技术 本文的研究内容着重于多光谱遥感图像的特征提取与比较上从光谱特征纹理特征形状特征三个方面进行研究理论与方法部分的创新和成果如下 (1) 光谱
2、特征 通过原始波段的点运算获得的图像中目标物的颜色及灰度或者波段间亮度的比较光谱特征对应于每个像素与像元的排列等空间结构无关本文采用的光谱特征提取方法采用基于改进 ISODATA(Iterative Self-Organizing Data Analysis Techniques A, 迭代自组织数据分析技术 A)算法的聚类分析方法与原算法相比改进算法的优点如下在保留原算法初始聚类的成果的基础上以类自身的状态作为合并与分裂是否进行的判定标准极大的降低了计算量将绝对性质的参数转变为比值使得原算法循环里面的参数动态化原算法叠代结束的条件是由叠代次数人为控制的改进算法是II以类自身达到一种内部平衡作
3、为叠代结束的判定标准的合理性更强 (2) 纹理特征一种反映图像像素灰度级空间分布的属性如果物体内部的灰度级变化明显又不是简单的色调变化那么该物体就有纹理本文采用的纹理特征提取方法采用基于最小二乘和区域分割技术的聚类分析方法该算法具有以下创新通过最小二乘法拟合的系数矢量是对单幅图像纹理表达的发展很好的表达了多光谱图像的纹理信息缩放法针对较复杂纹理的不规则性提出了对系数矢量进行调整的方法在区域分割的过程中提出了将开区域转化为闭区域和将闭区域规则化的方法 (3) 形状特征也称为轮廓特征是指整个图像或者图像中子对象的边缘特征和区域特征本文采用基于波段分组和不变矩的聚类分析方法提取形状特征该算法具有以下
4、创新提出并实现了基于传感器成像特性或者波段间相关程度将多光谱图像的波段分组的方法提出并实现了基于不变矩矢量来合并形状特征相似的区域的方法 (4) 特征比较在三种特征提取的基础上本文提出了四条矢量特征比较的标准用于比较两幅多光谱图像特征提取完成后的比较通过比较可以反映出两幅多光谱图像在光谱纹理形状特征上的相似程度 本文综合多光谱图像特征提取的常规方法提出了新的提取多光谱图像光谱纹理形状特征的方法并相应提出了比较两幅多光谱图像的特征矢量比较方法最后给出了 MATLAB 仿真实现结果 关键字多光谱遥感图像光谱特征纹理特征形状特征 IIIFEATURE EXTRACTION AND COMPARISO
5、N OF MULTI-SPECTRAL REMOTE SENSING IMAGES ABSTRACT Content-Based Image Retrieval (CBIR) is used to find out the target image from the image database according to the given image features. The image features can be extracted from the sample images provided or inputted by customers. CBIR mainly contai
6、ns four key techniques, which are image database, content description, feature extraction and matching and fast searching. This thesis deals with the feature extraction and comparison of multi-spectral remote sensing images. It contains three aspects, which are spectrum feature extraction, texture f
7、eature extraction and shape feature extraction. The main achievements and contributions about methods and algorithms are described as follows: (1) Spectrum feature is defined as the color or gray value of target in images, or comparison of Intensity between bands of the images. It corresponds to eve
8、ry pixel, and is independent to space structure. Improved ISODATA (Iterative Self-Organizing Data Analysis Techniques A) algorithm is provided to extract and IV compare the spectrum features of multi-spectral images. Using this algorithm, the computation is greatly reduced and the arguments turn to
9、be dynamic. The procedure of cluster splitting and merging is based on the result of primary classification. By changing absolute values into ratio values, dynamic parameters are realized to normalize the required parameters in the iteration. Without setting the number of the iteration, it is comple
10、ted until a balance is reached. (2) Texture feature is one of the attributes of image, which describes the space distribution of gray levels of image pixels. An image contains texture if the objects in the image have a distinct but not simple hue change. Texture feature extraction in this thesis is
11、based on Least Squares method and region segmentation. The contributions of this algorithm: the coefficient vectors achieved by Least Squares method properly express the texture information of the multi-spectral images, and the concept of texture in single-band image is developed to that of multi-sp
12、ectral images. The shrinking-expanding method is proposed to regulate the coefficient vectors because of the anomaly of complicated texture. In processing of region segmentation, a method of transforming the open region into close region and normalizing the closed region is proposed. (3) Shape featu
13、re is also called contour feature, which describes the edge characteristics of image or part of image. Shape feature extraction in this thesis is based on band grouping and moment invariants. A method of dividing the bands of multi-spectral images into groups is proposed and realized based on the at
14、tributes V of spectrograph or the correlation degree between the bands. And a method of merging the regions of similar shape feature is proposed and realized based on moment invariants. (4) Feature comparison: Based on the three feature extraction methods, four rules are designed to compare the feat
15、ures of the multi-spectral images. The comparison shows the similarity or the differences in spectrum, texture and shape features of two multi-spectral images. In this thesis, the usual methods on feature extraction of multi-spectral images are introduced, three new methods are designed to extract t
16、he spectrum, texture and shape features of multi-spectral images, and the comparison of the feature vectors of two multi-spectral images is proposed. Finally, the simulation of the three methods using MATLAB achieves good results. KEY WORDS: multi-spectral remote sensing images, spectrum feature, texture feature, shape feature 上海交通大学 学位论文原创性声明 本人郑重声明所呈交的学位论文是本人在导师的指导下独立进行研究工作所取得的成果 除文中已经注明引用的内容外 本论文不包含任何其他个人或集体已经发表或撰写过的作品成果 对本文的研究做出重要贡献的个人和集体 均已在文中以明确方式标明 本人完全意识到本声明的法律结果由本人承担 学位论文作者签名刘 磊 日期20
