【作者】 陈万海；

【导师】 赵春晖；

【作者基本信息】 哈尔滨工程大学 ， 信号与信息处理， 2008， 博士

【摘要】 随着数字信号处理技术、计算机技术以及通信技术的迅猛发展,遥感图像处理技术在军事、民用等领域发挥着越来越重要的作用。与多光谱遥感相比,超谱遥感具有更高的光谱分辨率。超谱图像分类的研究是超谱遥感应用的主要内容之一。硬分类是将复杂的现象简化为少量的类别,是提取超谱图像中有用信息的重要处理方法之一。另一方面,超谱图像的空间分辨率一般较低,从而导致混合像素广泛存在,而处理混合像素相对于处理纯像素更加困难且更具重要意义。作为混合像元处理主要技术的光谱分离,就是要去求解光谱分离后像元内各混合成分所占的比例,是一种更为精确的软分类技术。目前硬分类方法较多,但分类效果不够理想或是方法本身有待提升;传统软分类方法由于在光谱分离中无关类别的参与以及光谱分离模型本身的不足导致分离效果不够理想。为此,本文以支持向量机为主要理论,对超谱图像硬、软分类(光谱分离)及相关技术进行研究。第一,研究基于相似特征逐步删除的超谱图像波段选择和高斯低通滤波预处理方法。该波段选择方法建立在特征相似性计算之上,具有无监督性并且计算复杂度很小。所提出的高斯低通滤波方法旨在减弱或消除超谱遥感图像的高频分量但不影响低频分量,使得图像平滑且平滑后的图像类内距离变小而类间距离变大,以便有利于后续分类处理。第二,系统研究支持向量机相关理论,包括理论基础、分类原理、线性分类到非线性分类的推广、二类分类到多类分类的扩展、主要实现技术等,并且对其分类性能进行了实验测试。理论基础和主要原理的研究有利于理解支持向量机所具有的独特优势;发展类型和优化算法的研究旨在提高支持向量机的应用效率;多类实现方法的研究使得该技术能够更为方便地处理类别较多的分类问题。这部分内容的研究为后文开展奠定了必要的理论基础。第三,研究提高支持向量机分类精度的方法,包括分类前的模糊方法的应用,训练过程中加权方法的应用,以及初次训练完成后对于部分子分类器的二次训练思想等。模糊方法在模糊聚类的基础上选择训练样本,并采用支持向量机分类算法对图像进行最终的分类,用以克服样本选择的盲目性;加权方法采用距离尺度对最小二乘支持向量机惩罚项进行加权,以克服训练过程中噪声点的不良干扰;二次训练思想对两种类型的支持向量机和全部参数均进行有效的二次调整,通过对分类效果最差的几个子分类器进行二次参数调整,最后整合利用两次获得的最优参数赋予相应的子分类器构成新的总体分类器。第四,研究支持向量机应用于光谱分离即软分类的可行性和方法,以及光谱分离中有效的类别子集的选择方法。一方面,介绍了线性支持向量机应用于光谱分离的原理,应用的非线性扩展,并对其应用效果进行了论证;另一方面在光谱分离中分别根据空间相关性和感兴趣类别进行相关类别子集的选择,利用相关类别对混合像元进行更为精确的光谱分离。实验结果表明,支持向量机对于超谱图像的软、硬分类有着非常良好的效果;而恰当的预处理方法、支持向量机性能提升方法和类别子集选择有助于获得更好的分析效果。更多还原

【Abstract】 With the development of digital signal processing technology, computer technology and communication technology, remote sensing imagery processing takes increasingly important effects in the fields of military and civil applications. Compared with multispectral remote sensing, the hyperspectral remote sensing has a higher spectral resolution. The research of hyperspectral images (HSI) classification is one of the main contents of the hyperspectral remote sensing application. Hard classification which aims at simplifying the complex phenomenon is one of the significant processing methods in the interest information extraction of hyperspectral images. On the other hand, mixed pixels are widely existent in hyperspectral images for its low spatial resolution. The analysis and processing of mixed pixels are of more importance and significance. As a main technique of mixed pixel processing, spectral unmixing is to work out the mixing proportion of each class included in mixed pixel. It is a more accurate soft classification technique. At present, there are lots of hard classification methods, but their classification performances are not very perfect, or some methods themselves are to be improved. Traditional spectral unmixing methods are inefficient for the participation of unrelated classes and for the deficiency of spectral unmixing model. In this case, the techniques of hard classification and soft classification (spectral unmixing) are researched mainly based on support vector machine (SVM) theory in the paper.Firstly, a band selection method based on stepwise deletion of similar feature and a gauss low pass filtering method are proposed. The band selection method is constructed on the computation of feature similarity. The method is unsupervised and of low computational complexity. The proposed gauss low pass filtering method aims to weaken or eliminate the high frequency component of hyperspectral images but not change its low frequency component. The filter is designed to smooth hyperspectral, to reduce within-class distance and to enlarge between-class distance. In this case, the filtering method is helpful of following classification processing of hyperspectral images.Secondly, SVM theory is researched systemically, including theory basis, classification principle, the generalization form linear classification to nonlinear classification, the extent from bi-class problem to multi-class problem, and the main implementation techniques. The classification performance of SVM is also tested in this paper. The studies of theory basis and classification principle benefit to understand the unique dominance of SVM, the studies of optimization algorithm and development version are helpful of improving its application efficiency, and the study of multi-class extension makes it possible for the technique to process classification problem with large number of classes conveniently. These studies provide necessary theory basis for the progress of the dissertation.Thirdly, the classification performance of SVM is improved; including the usage of fuzzy method before classification, distance based fuzzy SVM, and secondary training in multi-class problem. In order to overcome the blindness of sample selection, fuzzy method select training samples based on fuzzy xxx, followed by SVM based classification. The weighted SVM makes use of distance measure to fuzzy weigh the punishment terms in least square SVM, and so overcomes the bad influence brought by outliers in process of training. In the idea of secondary training, all parameters in two kinds of SVM are regulated effectively. Through the secondary parameter regulation for some weak classifiers, new general classifier is formed by sub-classifiers with optimal parameters.Finally, the feasibility and method of applying SVM to spectral unmixing (a kind of soft classification) and the selection method of class sub-set are researched. On the one hand, the principle of applying linear SVM to spectral unmixing is introduced, and then the application is intended to nonlinear SVM. The unmixing effect of the application is also tested. On the other hand, the selection of correlative class sub-set is conducted based on spatial correlation and on class-of-interest is researched, and the more accuracy spectral unmixing is implemented on the class sub-set.Experiments show that SVM has good performance both in hard classification and in soft classification, and proper preprocessing measure, improving measure of SVM, and selection measure of class sub-set are all helpful of getting better analysis effect.更多还原