毕业论文 多时相遥感影像变化检测算法研究.doc
约63页DOC格式手机打开展开
毕业论文 多时相遥感影像变化检测算法研究,摘要利用多时相遥感影像获取地物变化信息的过程称之为变化检测。根据影像分析的层次不同，变化检测算法可以分为像素级、特征级和目标级这三类；根据数据分析的机理，变化检测算法可以分为有监督和无监督两类。有监督的变化检测算法是基于有监督的分类方法，这种方法要求训练网络以得到网络的参数。无监督的变化检测算法用两张不同时相的遥感影像...
内容介绍
此文档由会员 ljjwl8321 发布
摘要
利用多时相遥感影像获取地物变化信息的过程称之为变化检测。根据影像分析的层次不同，变化检测算法可以分为像素级、特征级和目标级这三类；根据数据分析的机理，变化检测算法可以分为有监督和无监督两类。有监督的变化检测算法是基于有监督的分类方法，这种方法要求训练网络以得到网络的参数。无监督的变化检测算法用两张不同时相的遥感影像通过直接的比较而无需附加信息就可以检测出影像的变化。本文所提众多算法都是基于像素级、无监督的变化检测算法。
本文提出了一种基于主分量分析和上下截集模糊Kohonen聚类网络的无监督的不同时相的遥感影像的像素级变化检测算法。该算法首次将主分量分析和上下截集模糊Kohonen聚类网络这两种方法相结合，并将它应用于不同时相的遥感影像变化检测。该方法结合每个象素的邻域信息，利用主分量分析，产生每个象素对应的基于邻域信息的特征向量；又将变化区域检测问题转化为两类之间的分类问题；然后利用上下截集模糊 Kohonen 聚类网络对每个象素所对应的特征向量进行变化类与未变化类的聚类，得到变化检测图。
本文又提出了一种基于非下采样Contourlet变换和脉冲耦合神经网络的无监督的不同时相的遥感影像的变化检测算法。该算法将非下采样Contourlet变换和脉冲耦合神经网络这两种方法相结合，并首次将它应用于不同时相的遥感影像变化检测。
本文首次将非下采样Contourlet变换和上下截集模糊Kohonen聚类网络相结合，提出了一种无监督的多时相遥感影像变化检测算法。该算法采用非下采样Contourlet变换提取与对数比图像中的每个象素相对应的多尺度、多方向纹理，并采用上下截集模糊Kohonen聚类网络将这些多尺度、多方向纹理分为变化类与未变化类两类，最终得到变化检测图。
通过三个具体的变化检测算法的研究，归纳出变化检测算法一般研究思路。
关键词：主分量分析；上下截集模糊 Kohonen 聚类网络；非下采样Contourlet变换；脉冲耦合神经网络；无监督变化检测；多尺度多方向；多时相遥感影像；遥感
Abstract
The process of obtaining the changed information of the earth by making use of multi-temporal satellite images is called change detection. According to the level of analyzing image, the change detection algorithms can be divided into pixel level class, characteristic level one and target level one. According to the mechanism of processing data, they can be divided into supervised class and unsupervised one. The kind of the supervised change detection algorithms are based on method of supervised classifying and require training to get the parameters of network. While the kind of the unsupervised change detection algorithms generate the change map by making a comparison of bi-temporal satellite images automatically without manual operation. The proposed algorithms belong to the kind of unsupervised change detection algorithms in pixel level.
An unsupervised change detection algorithm in multi-temporal satellite images based on principal component analysis and up-down-set fuzzy Kohonen clustering network is proposed. This method makes a combination of both PCA and UDSFKCN initially, and applies it to change detection. This method generates eigenvector corresponding to every pixel combining itself with its neighbors using principal component analysis. At the same time, solving the detection of the changed pixel in a region is to divide the pixel into two groups, changed class and unchanged class. Since every pixel is described as a eigenvector, therefore to obtain a changed map of the changed region in pixel level, up-down-set fuzzy Kohonen clustering network is applied to divide all the eigenvectors into changed ones and unchanged ones.
An unsupervised change detection algorithm in multi-temporal satellite images based on non-sub-sampled Contourlet transform and pulse coupled neural network is proposed. This method makes a combination of both non-sub-sampled Contourlet transform and pulse coupled neural network, and applies it to change detection initially.
An unsupervised multi-scale change detection algorithm in multi-temporal satellite images is also proposed. This method makes a combination of both non-sub-sampled Contourlet transform and up-down-set fuzzy Kohonen clustering network, and applies it to change detection initially. For each pixel in the log-ratio image, multi-scale and multi-direction feature vector is extracted using non-sub-sampled Contourlet transform. The final change detection map is achieved by clustering the multi-scale and multi-direction feature vectors using up-down-set fuzzy Kohonen clustering network into two classes: changed and unchanged.
Through three specific change detection algorithms, summarized the change detection algorithm for general research ideas.
Keywords：Principal Component Analysis(PCA); Up-Down-Set Fuzzy Kohonen Clustering Network(UDSFKCN); Non-sub-sampled Contourlet Transform (NSCT); Pulse Coupled Neural Network (PCNN); Unsupervised Change Detection; Multi-scale and Multi-direction; Multi-temporal Satellite Images; Remote Sensing
目录
第一章 引言 1
1.1 研究背景及意义 1
1.1.1背景 1
1.1.2意义 2
1.2 国内图像变化检测经典算法 4
1.2.1主分量分析法 4
1.2.2最大类间方差法 5
1.2.3最小二乘图像相减法 6
1.2.4小波与FCM结..
利用多时相遥感影像获取地物变化信息的过程称之为变化检测。根据影像分析的层次不同，变化检测算法可以分为像素级、特征级和目标级这三类；根据数据分析的机理，变化检测算法可以分为有监督和无监督两类。有监督的变化检测算法是基于有监督的分类方法，这种方法要求训练网络以得到网络的参数。无监督的变化检测算法用两张不同时相的遥感影像通过直接的比较而无需附加信息就可以检测出影像的变化。本文所提众多算法都是基于像素级、无监督的变化检测算法。
本文提出了一种基于主分量分析和上下截集模糊Kohonen聚类网络的无监督的不同时相的遥感影像的像素级变化检测算法。该算法首次将主分量分析和上下截集模糊Kohonen聚类网络这两种方法相结合，并将它应用于不同时相的遥感影像变化检测。该方法结合每个象素的邻域信息，利用主分量分析，产生每个象素对应的基于邻域信息的特征向量；又将变化区域检测问题转化为两类之间的分类问题；然后利用上下截集模糊 Kohonen 聚类网络对每个象素所对应的特征向量进行变化类与未变化类的聚类，得到变化检测图。
本文又提出了一种基于非下采样Contourlet变换和脉冲耦合神经网络的无监督的不同时相的遥感影像的变化检测算法。该算法将非下采样Contourlet变换和脉冲耦合神经网络这两种方法相结合，并首次将它应用于不同时相的遥感影像变化检测。
本文首次将非下采样Contourlet变换和上下截集模糊Kohonen聚类网络相结合，提出了一种无监督的多时相遥感影像变化检测算法。该算法采用非下采样Contourlet变换提取与对数比图像中的每个象素相对应的多尺度、多方向纹理，并采用上下截集模糊Kohonen聚类网络将这些多尺度、多方向纹理分为变化类与未变化类两类，最终得到变化检测图。
通过三个具体的变化检测算法的研究，归纳出变化检测算法一般研究思路。
关键词：主分量分析；上下截集模糊 Kohonen 聚类网络；非下采样Contourlet变换；脉冲耦合神经网络；无监督变化检测；多尺度多方向；多时相遥感影像；遥感
Abstract
The process of obtaining the changed information of the earth by making use of multi-temporal satellite images is called change detection. According to the level of analyzing image, the change detection algorithms can be divided into pixel level class, characteristic level one and target level one. According to the mechanism of processing data, they can be divided into supervised class and unsupervised one. The kind of the supervised change detection algorithms are based on method of supervised classifying and require training to get the parameters of network. While the kind of the unsupervised change detection algorithms generate the change map by making a comparison of bi-temporal satellite images automatically without manual operation. The proposed algorithms belong to the kind of unsupervised change detection algorithms in pixel level.
An unsupervised change detection algorithm in multi-temporal satellite images based on principal component analysis and up-down-set fuzzy Kohonen clustering network is proposed. This method makes a combination of both PCA and UDSFKCN initially, and applies it to change detection. This method generates eigenvector corresponding to every pixel combining itself with its neighbors using principal component analysis. At the same time, solving the detection of the changed pixel in a region is to divide the pixel into two groups, changed class and unchanged class. Since every pixel is described as a eigenvector, therefore to obtain a changed map of the changed region in pixel level, up-down-set fuzzy Kohonen clustering network is applied to divide all the eigenvectors into changed ones and unchanged ones.
An unsupervised change detection algorithm in multi-temporal satellite images based on non-sub-sampled Contourlet transform and pulse coupled neural network is proposed. This method makes a combination of both non-sub-sampled Contourlet transform and pulse coupled neural network, and applies it to change detection initially.
An unsupervised multi-scale change detection algorithm in multi-temporal satellite images is also proposed. This method makes a combination of both non-sub-sampled Contourlet transform and up-down-set fuzzy Kohonen clustering network, and applies it to change detection initially. For each pixel in the log-ratio image, multi-scale and multi-direction feature vector is extracted using non-sub-sampled Contourlet transform. The final change detection map is achieved by clustering the multi-scale and multi-direction feature vectors using up-down-set fuzzy Kohonen clustering network into two classes: changed and unchanged.
Through three specific change detection algorithms, summarized the change detection algorithm for general research ideas.
Keywords：Principal Component Analysis(PCA); Up-Down-Set Fuzzy Kohonen Clustering Network(UDSFKCN); Non-sub-sampled Contourlet Transform (NSCT); Pulse Coupled Neural Network (PCNN); Unsupervised Change Detection; Multi-scale and Multi-direction; Multi-temporal Satellite Images; Remote Sensing
目录
第一章 引言 1
1.1 研究背景及意义 1
1.1.1背景 1
1.1.2意义 2
1.2 国内图像变化检测经典算法 4
1.2.1主分量分析法 4
1.2.2最大类间方差法 5
1.2.3最小二乘图像相减法 6
1.2.4小波与FCM结..
