摘要
利用多時相遙感影像獲取地物變化信息的過程稱之為變化檢測。根據(jù)影像分析的層次不同，變化檢測算法可以分為像素級、特征級和目標(biāo)級這三類；根據(jù)數(shù)據(jù)分析的機理，變化檢測算法可以分為有監(jiān)督和無監(jiān)督兩類。有監(jiān)督的變化檢測算法是基于有監(jiān)督的分類方法，這種方法要求訓(xùn)練網(wǎng)絡(luò)以得到網(wǎng)絡(luò)的參數(shù)。無監(jiān)督的變化檢測算法用兩張不同時相的遙感影像通過直接的比較而無需附加信息就可以檢測出影像的變化。本文所提眾多算法都是基于像素級、無監(jiān)督的變化檢測算法。
本文提出了一種基于主分量分析和上下截集模糊Kohonen聚類網(wǎng)絡(luò)的無監(jiān)督的不同時相的遙感影像的像素級變化檢測算法。該算法首次將主分量分析和上下截集模糊Kohonen聚類網(wǎng)絡(luò)這兩種方法相結(jié)合，并將它應(yīng)用于不同時相的遙感影像變化檢測。該方法結(jié)合每個象素的鄰域信息，利用主分量分析，產(chǎn)生每個象素對應(yīng)的基于鄰域信息的特征向量；又將變化區(qū)域檢測問題轉(zhuǎn)化為兩類之間的分類問題；然后利用上下截集模糊 Kohonen 聚類網(wǎng)絡(luò)對每個象素所對應(yīng)的特征向量進行變化類與未變化類的聚類，得到變化檢測圖。
本文又提出了一種基于非下采樣Contourlet變換和脈沖耦合神經(jīng)網(wǎng)絡(luò)的無監(jiān)督的不同時相的遙感影像的變化檢測算法。該算法將非下采樣Contourlet變換和脈沖耦合神經(jīng)網(wǎng)絡(luò)這兩種方法相結(jié)合，并首次將它應(yīng)用于不同時相的遙感影像變化檢測。
本文首次將非下采樣Contourlet變換和上下截集模糊Kohonen聚類網(wǎng)絡(luò)相結(jié)合，提出了一種無監(jiān)督的多時相遙感影像變化檢測算法。該算法采用非下采樣Contourlet變換提取與對數(shù)比圖像中的每個象素相對應(yīng)的多尺度、多方向紋理，并采用上下截集模糊Kohonen聚類網(wǎng)絡(luò)將這些多尺度、多方向紋理分為變化類與未變化類兩類，最終得到變化檢測圖。
通過三個具體的變化檢測算法的研究，歸納出變化檢測算法一般研究思路。
關(guān)鍵詞：主分量分析；上下截集模糊 Kohonen 聚類網(wǎng)絡(luò)；非下采樣Contourlet變換；脈沖耦合神經(jīng)網(wǎng)絡(luò)；無監(jiān)督變化檢測；多尺度多方向；多時相遙感影像；遙感

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




















目錄
第一章 引言 1
1.1 研究背景及意義 1
1.1.1背景 1
1.1.2意義 2
1.2 國內(nèi)圖像變化檢測經(jīng)典算法 4
1.2.1主分量分析法 4
1.2.2最大類間方差法 5
1.2.3最小二乘圖像相減法 6
1.2.4小波與FCM結(jié)..