本文選題：高分影像 + 變化檢測　； 參考：《中國地質(zhì)大學(xué)》2017年博士論文

【摘要】：快速準確地從滑坡數(shù)據(jù)中尋找到災(zāi)害發(fā)生區(qū)域,并標示出受災(zāi)范圍和程度等信息對于開展災(zāi)后救援具有重要意義。遙感技術(shù)可以不進入現(xiàn)場即可獲得災(zāi)情信息,在滑坡災(zāi)后救援方面得到了重要應(yīng)用。然而,由于災(zāi)損地區(qū)往往地表覆蓋復(fù)雜多變,故在缺少輔助數(shù)據(jù)的情況下難以僅利用災(zāi)后影像通過分類的方式提取災(zāi)情信息。隨著遙感技術(shù)的發(fā)展,目前基于雙時相影像的變化檢測技術(shù)得到了快速發(fā)展。利用災(zāi)前災(zāi)后影像結(jié)合變化檢測方法可以更為直觀地顯示災(zāi)損情況,因此發(fā)展基于變化檢測原理的滑坡信息提取方法具有十分重要的意義。近年來高分辨率影像得到了迅速的發(fā)展,并被廣泛應(yīng)用于資源調(diào)查、數(shù)據(jù)庫更新、災(zāi)后救援等領(lǐng)域。早期的變化檢測算法研究都是針對中低分辨率影像開展的,現(xiàn)有針對高分影像的算法還鮮有應(yīng)用于滑坡研究的文獻報道,且方法一般會針對某種類型的變化,故當將其應(yīng)用于滑坡信息提取時會存在諸多方面的問題,造成提取結(jié)果精度低下。因此需要開展基于高分辨率影像的變化檢測方法在滑坡提取方面的研究,以滿足災(zāi)后救援對滑坡信息快速、高精度提取的迫切要求。由于高分影像變化檢測面臨數(shù)據(jù)量龐大和影像分割尺度難以精準確定問題,且在提取變化信息時閾值也難以最佳獲取,現(xiàn)有研究一定程度上可以解決上述問題,但方法的適應(yīng)性等有待深入探討。基于此,本文開展了基于變化檢測原理的高分辨率影像滑坡信息提取研究,完善了基于高分影像在像素和對象不同層面的滑坡信息提取及閾值自動選取理論。本文主要研究內(nèi)容及創(chuàng)新點如下:1)以像素為處理單位,將ICA原理引入到滑坡提取研究,提出了基于ICA/MNF原理的提取方法。由于地表覆蓋差異、噪聲等問題,尤其是災(zāi)后數(shù)據(jù)中地表雜亂無章,影像往往不會服從高斯分布,這與PCA、CCA等常用方法以服從高斯分布為基礎(chǔ)的基本假設(shè)不相符,則運用此類方法提取相關(guān)信息難以取得最好的提取效果。而ICA方法以信號的非高斯性為基礎(chǔ),故而用其提取影像的獨立成分是十分有利的。本文針對遙感影像波段之間存在信息冗余且高分影像數(shù)據(jù)量龐大的問題,提出了基于ICA/MNF的變化信息提取方法。算法以單一時相的影像為基礎(chǔ),對雙時相影像分別運用基于負熵最大化的Fast-ICA算法分離出相互正交的獨立成分,并構(gòu)建對應(yīng)獨立成分的差異成分。由于變化信息分布于多個成分中,為利用少量的成分獲取到最大的變化信息,本文使用MNF算法對差異成分進行了變化信息的集中,通過設(shè)置滑動閾值獲取了差異影像的閾值,最終提取了滑坡信息。通過對兩組數(shù)據(jù)進行實驗證實了方法的可行性,災(zāi)害提取結(jié)果的整體精度和Kappa精度分別達到了75%和0.47以上;同時與基于像素的差異主成分法、波段差異法兩種方法進行了實驗對比,驗證了方法的優(yōu)越性。2)開展了面向?qū)ο蠡赗F原理的全特征、優(yōu)選特征影像分類研究,通過對比災(zāi)前災(zāi)后影像的對象類別提取了滑坡信息,同時與SVM、KNN算法進行了對比分析,得出了部分有益的結(jié)論。由于以像素為單位進行滑坡提取存在嚴重的“椒鹽”效應(yīng),且在提取的連續(xù)變化區(qū)域中存在“空洞”現(xiàn)象,從而影響最終結(jié)果的精度。本文深入分析了影像分割理論,對疊加的災(zāi)前災(zāi)后影像經(jīng)過多次嘗試獲取了較優(yōu)分割尺度并進行了分割,提取了對象的光譜、紋理、形狀、語義等41個特征;為使用盡量少的特征實現(xiàn)影像的高精度分類,本文使用VarSelRF程序包進行特征優(yōu)選方面的分析,統(tǒng)計分析了優(yōu)選特征重要性排序、被選中次數(shù)的關(guān)系,獲取了不同影像的優(yōu)選特征;使用對象的全特征與優(yōu)選特征分別進行了RF分類研究,并與SVM、KNN算法進行了對比分析,結(jié)果表明RF分類效果明顯優(yōu)于SVM、KNN算法,SVM結(jié)果最差,且整體上RF全特征分類精度稍優(yōu)于優(yōu)選特征,單一影像分類的整體精度和Kappa精度可分別達到94%和0.89以上;最后結(jié)合分類后比較思想,利用優(yōu)選特征的RF分類結(jié)果提取了滑坡信息,結(jié)果的整體精度和Kappa精度分別提高到了77%和0.59,變化類的生產(chǎn)者精度較基于像素的ICA/MNF方法得到了提升,一定程度上抑制了漏檢滑坡的現(xiàn)象。獲得如下結(jié)論:I.在特征優(yōu)選方面:a)被選特征與地表覆蓋有重要關(guān)系,而不同時相、不同種類影像之間有一定的差異性;b)被選中次數(shù)與特征重要性具有一定關(guān)系,重要性排名前五的特征在整體上的被選中次數(shù)表現(xiàn)為高頻,反之亦然;c)高分影像對象中,紋理和形狀特征一般是分類中不可或缺的,但當分辨率較低時,紋理和形狀信息則不突出,一般表現(xiàn)為極低的被選概率;II.在分類方面:a)全特征的分類精度整體上稍優(yōu)于優(yōu)選特征,但二者差別不大;b)將影像分為若干個精確地類時的分類精度明顯優(yōu)于二分類問題,提高二分類精度的可能解決辦法是添加其它輔助數(shù)據(jù)或是加大樣本的種類和各個類別樣本的數(shù)量;III.在應(yīng)用分類后比較法提取滑坡時,受單一類別分類精度影響,連續(xù)變化區(qū)域一般可以取得較好的結(jié)果,而在不連續(xù)區(qū)域效果極差。3)開展了影像分割尺度研究,提出了多序列影像對象的概念,以子對象為處理單位進行了變化檢測和滑坡提取研究。針對面向?qū)ο蠓椒ㄖ凶顑?yōu)尺度難以獲取但其對結(jié)果至關(guān)重要的問題,本文深入分析了面向?qū)ο笥跋穹指钪须y以確定最優(yōu)分割尺度問題,提出了多序列影像對象的概念,將等差數(shù)列引入到影像分割領(lǐng)域用于影像分割尺度參數(shù)的生成,進行了面向?qū)ο蟮幕滦畔⑻崛⊙芯�。算法以單一時相的影像為基礎(chǔ)分別對其進行多個單一尺度的有序分割,以便觀察對象在空間的變化規(guī)律,并以雙時相影像的最小分割尺度為基準分裂獲取子對象,以子對象為單位搜索其在雙時相影像各個分割尺度層中的關(guān)聯(lián)對象,通過構(gòu)建變化特征向量并獲取閾值,根據(jù)變化特征向量的大小確定子對象是否發(fā)生了變化,并最終獲得變化信息。結(jié)合對兩組數(shù)據(jù)進行實驗,滑坡提取結(jié)果的整體精度和Kappa精度分別達到了85%和0.68,表明提出方法是切實可行的;通過與以像素為單位的變化向量方法和面向?qū)ο蠓椒▽Ρ?驗證了提出方法的優(yōu)越性,以及與前述基于ICA/MNF方法和面向?qū)ο驲F分類后比較法的結(jié)果對比分析,發(fā)現(xiàn)整體提取精度不僅是最高的,且滑坡類的用戶精度提升到了76%,有效抑制了提取結(jié)果中的誤檢現(xiàn)象。4)開展了影像閾值選取研究,改進了蜂群算法并用于最優(yōu)閾值自動獲取�；谇拔牡姆治龊蛯嶒灲Y(jié)果,閾值對于滑坡信息具有至關(guān)重要的影響,選擇不當會極大的損害算法最終結(jié)果的精度。人工蜂群算法具有控制參數(shù)少、計算簡便、全局搜索能力強等優(yōu)勢。為快速高效地進行圖像分割,針對人工蜂群算法存在的收斂速度慢、易陷入局部最優(yōu)解等問題,提出了一種基于改進人工蜂群算法分割二維OTSU圖像的新方法。算法通過對蜜源更新過程中向當前最優(yōu)蜜源方向進行引導(dǎo),加快了算法的收斂速度;為避免算法陷入局部最優(yōu)并加快收斂速度,在對當前最優(yōu)解附近局部搜索過程中動態(tài)縮減了搜索范圍,加大了更優(yōu)解被發(fā)現(xiàn)的概率;針對較大梯度值無意義的問題,限定了蜜源范圍,提高了算法的效率。以灰度-梯度二維直方圖中背景類和目標類的方差-協(xié)方差矩陣的跡為測度函數(shù),結(jié)合具有不同直方圖分布的圖像進行了實驗,統(tǒng)計不同算法在各個影像獲得最優(yōu)解的用時和迭代次數(shù)等信息量,結(jié)果表明改進算法具有穩(wěn)健、高效、快速的特性;同時發(fā)現(xiàn)算法對標準測試圖像和滑坡提取均具有較好的分割效果,且在含有噪聲情況下算法對滑坡提取比測試圖像具有相對較優(yōu)的結(jié)果;通過與改進算法但未限制蜜源生成范圍、經(jīng)典ABC算法且不限制蜜源范圍兩種方法的對比,實驗顯示改進算法在獲得最優(yōu)解時的迭代次數(shù)、整體運行時間以及獲得最優(yōu)解時的用時三個方面均明顯優(yōu)于對比方法,證明了改進算法的優(yōu)越性。
[Abstract]:It is of great significance to quickly and accurately find the area of the disaster from the landslide data and to indicate the extent and degree of the disaster. The remote sensing technology can obtain the disaster information without entering the site, and it is important to be used after the landslide disaster relief. With the development of the remote sensing technology, the technology of change detection based on the dual phase image has been developed rapidly. Using the pre disaster post disaster image combined with the change detection method can show the damage situation more intuitively, with the development of remote sensing technology. Therefore, it is very important to develop the method of landslide information extraction based on the principle of change detection. In recent years, the high resolution image has been developed rapidly, and is widely used in the fields of resource investigation, database updating, disaster relief and so on. The research of early change detection algorithms is carried out for low and medium resolution images. There are few literature reports applied to the study of landslide research, and the method generally aims at some kind of change, so when it is applied to the extraction of landslide information, there will be many problems, resulting in the low precision of the extraction results. Therefore, a change detection method based on high resolution image is needed to carry out the landslide extraction. In order to meet the urgent requirements of rapid and high precision extraction of landslide information after disaster relief, it is difficult to accurately determine the high resolution image change detection because of the huge amount of data and the image segmentation scale, and the threshold is difficult to obtain when the change information is extracted. The existing research can solve the above problems to some extent. However, the adaptability of the method needs to be discussed in depth. Based on this, this paper carries out the study of high resolution image landslide information extraction based on the principle of change detection, and improves the theory of landslide information extraction and threshold automatic selection based on high resolution images at different levels of pixels and objects. The main research content and innovation points are as follows: 1) pixels For the processing unit, the ICA principle is introduced to the study of landslide extraction, and an extraction method based on the principle of ICA/MNF is put forward. Due to the difference of surface coverage and noise, especially in the post disaster data, the image often does not obey the Gauss distribution, which is not based on the basic assumption that the common methods such as PCA and CCA are based on the distribution of Gauss. It is difficult to obtain the best extraction effect by using this method, and the ICA method is based on the non Gauss character of the signal, so it is very beneficial to extract the independent components of the image. This paper proposes a ICA/MN based on the problem of the information redundancy and the large amount of image data between the remote sensing image bands. The method of extracting the change information of F. Based on the image of a single phase, the Fast-ICA algorithm based on the maximum of negative entropy is used to separate the independent components of each other, and the difference components corresponding to the independent components are constructed. In this paper, the MNF algorithm is used to focus the variation information on the difference components. The threshold of the difference image is obtained by setting the sliding threshold, and the landslide information is extracted. The feasibility of the method is verified by the experiment of two groups of data. The overall accuracy and the Kappa precision of the disaster extraction results are 75% and 0.47 respectively. At the same time, compared with two methods based on pixel differential principal component and band difference method, the superiority of the method is verified by the two methods. The whole feature based on the object oriented RF principle is carried out, the feature image classification is optimized, and the landslide information is extracted by comparing the object categories before the disaster, and the SVM, KNN algorithm is also obtained. A comparative analysis is carried out and some useful conclusions are drawn. Because of the serious "salt and pepper" effect in the extraction of landslides in pixels, there is a "hollow" phenomenon in the continuous changing region of the extraction, thus affecting the accuracy of the final result. After many times, we try to get the better segmentation scale and divide it, extract the 41 features of the spectral, texture, shape, and semantics of the object. In order to realize the high precision classification of the image by using as few features as possible, this paper uses the VarSelRF package to analyze the feature selection, and statistics and analyze the priority order of the selected features, and the selected time is selected. The relationship between the number and the optimal feature of different images is obtained. The RF classification is carried out with the full features of the object and the preferred feature, and the comparison analysis is carried out with the SVM and KNN algorithm. The results show that the RF classification effect is obviously better than the SVM, KNN algorithm and the worst result of SVM, and the overall classification accuracy of RF is slightly better than the optimal feature, and the single image is a single image. The overall accuracy and Kappa precision of the classification can reach 94% and more than 0.89 respectively. Finally, the landslide information is extracted with the RF classification results of the selected features. The overall accuracy and the Kappa accuracy of the results are increased to 77% and 0.59 respectively. The producer precision of the change class is improved compared with the pixel based ICA/MNF method. To a certain degree, the phenomenon of landslide is suppressed to a certain extent. The following conclusions are obtained: I. in feature selection: a) the selected feature is closely related to the surface coverage, but the difference between different types of images is different; b) has a certain relationship with the feature importance, the first five characteristics of the importance ranking are selected as a whole. In the high score image objects, the texture and shape features are generally indispensable in the classification, but when the resolution is low, the texture and shape information is not prominent and generally shows a very low probability of selection; the classification accuracy of the II. in the classification aspect: a) is a little better than the preferred feature, but the two are two. The difference is not significant; b) the classification accuracy of the image classification is obviously superior to the two classification problem, and the possible solution to improve the two classification accuracy is to add other auxiliary data or to increase the type of samples and the number of samples in each category; III. is subject to a single classification precision when the landslide is extracted after the application of classification. As a result, better results can be obtained in the continuous changing region, while the image segmentation scale is studied in the discontinuous region of.3). The concept of multi sequence image objects is proposed, and the research of change detection and landslide extraction is carried out with the sub object as the processing unit. The problem is very important. This paper deeply analyzes the problem that is difficult to determine the optimal segmentation scale in object image segmentation. The concept of multi sequence image object is put forward, and the arithmetic sequence is introduced into the image segmentation field for the generation of image segmentation scale parameters, and the landslide information extraction is studied. The algorithm is single. The image of the time phase is based on the sequential segmentation of multiple single scales, so as to observe the variation of the object in the space, and take the minimum segmentation scale of the dual phase image as the base division to obtain the sub objects, and search the related objects in each cut scale layer of the dual phase image by the sub object, and build the change through the construction. According to the size of the change feature vector, it determines whether the subobjects change, and finally obtains the change information. The overall accuracy and Kappa accuracy of the landslide extraction results are 85% and 0.68 respectively. The results show that the proposed method is feasible, and the method is based on pixels. The comparison between the change vector method and the object oriented method verifies the superiority of the proposed method, and the comparison analysis of the results based on the ICA/MNF method and the object oriented RF classification method. It is found that the overall extraction precision is not only the highest, but the user accuracy of the landslide class is raised to 76%, and the error detection in the extraction results is effectively suppressed. Phenomenon.4) carry out the study of image threshold selection, improve the colony algorithm and use the optimal threshold automatically. Based on the previous analysis and experimental results, the threshold value has a crucial influence on the landslide information, and the accuracy of the final result of the damage algorithm is very large. The artificial bee colony algorithm has less control parameters and simple calculation. In order to segment the image quickly and efficiently, in order to solve the problem of slow convergence and local optimal solution of artificial bee colony algorithm, a new method based on improved artificial bee colony algorithm to segment two-dimensional OTSU images is proposed. The algorithm is directed to the current optimal nectar source in the process of honeysource update. In order to avoid the local optimization and speed up the convergence speed of the algorithm, the search range is reduced dynamically in the local search process near the current optimal solution, and the probability of finding the better solution is increased in the process of local search near the current optimal solution; the nectar source range is limited to the larger gradient value and the efficiency of the algorithm is improved. The trace of the variance covariance matrix of the background class and the target class in the gray-scale gradient two-dimensional histogram is the measure function. The experiments are carried out with the images of different histogram distribution, and the information amount of the optimal solution and the number of iterations of the different algorithms are obtained. The results show that the improved algorithm is robust, efficient and fast. At the same time, it is found that the algorithm has better segmentation effect on the standard test image and landslide extraction, and the algorithm has a relatively superior result on the landslide extraction compared with the test image under the condition of noise, and the comparison of the two methods of the classic ABC algorithm and the nectar source range without limiting the range of the nectar source generation with the improved algorithm is compared with the improved algorithm. It is proved that the improved algorithm is superior to the contrast method in three aspects, the number of iterations, the overall running time and the time of obtaining the optimal solution.
【學(xué)位授予單位】：中國地質(zhì)大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2017
【分類號】：P237;P642.22

【相似文獻】

相關(guān)期刊論文 前10條

1 劉直芳,張繼平;變化檢測方法及其在城市中的應(yīng)用[J];測繪通報;2002年09期

2 李德仁;利用遙感影像進行變化檢測[J];武漢大學(xué)學(xué)報(信息科學(xué)版);2003年S1期

3 胡巖峰,劉波,李峰,李立鋼,丘江;一種基于統(tǒng)計分析的圖像變化檢測方法[J];光子學(xué)報;2005年01期

4 宋翠玉;李培軍;楊鋒杰;;運用多尺度圖像紋理進行城市擴展變化檢測[J];國土資源遙感;2006年03期

5 李雪;舒寧;王琰;李亮;;利用土地利用狀態(tài)轉(zhuǎn)移分析的變化檢測[J];武漢大學(xué)學(xué)報(信息科學(xué)版);2011年08期

6 方針,張劍清,張祖勛;基于城區(qū)航空影像的變化檢測[J];武漢測繪科技大學(xué)學(xué)報;1997年03期

7 徐宏根;宋妍;;顧及陰影信息的高分辨率遙感圖像變化檢測方法[J];國土資源遙感;2013年04期

8 馬云飛;李宏;;遙感變化檢測技術(shù)方法綜述[J];測繪與空間地理信息;2014年01期

9 劉直芳,張劍清;城區(qū)變化檢測的一種方法[J];測繪通報;2001年02期

10 倪林,冷洪超;機場區(qū)域變化檢測研究[J];遙感技術(shù)與應(yīng)用;2002年04期

相關(guān)會議論文 前10條

1 尤紅建;詹芊芊;;尺度優(yōu)化的星載SAR圖像變化檢測[A];中國測繪學(xué)會第九次全國會員代表大會暨學(xué)會成立50周年紀念大會論文集[C];2009年

2 劉元波;;環(huán)境遙感變化探測研究中的若干問題：輻射校正方法與變化檢測算法及其理論關(guān)系[A];中國地理學(xué)會2007年學(xué)術(shù)年會論文摘要集[C];2007年

3 邢帥;徐青;;高分辨率衛(wèi)星遙感影像變化檢測技術(shù)的研究[A];第十三屆全國圖象圖形學(xué)學(xué)術(shù)會議論文集[C];2006年

4 霍春雷;程健;周志鑫;盧漢清;;基于尺度傳播的多尺度變化檢測新方法[A];第十四屆全國圖象圖形學(xué)學(xué)術(shù)會議論文集[C];2008年

5 張蔚然;韓萍;;基于去取向理論的極化SAR變化檢測[A];第二十五屆中國（天津）2011’IT、網(wǎng)絡(luò)、信息技術(shù)、電子、儀器儀表創(chuàng)新學(xué)術(shù)會議論文集[C];2011年

6 胡艷;李勝;何宗;羅靈軍;李靜;;水體變化檢測在重慶市干旱遙感監(jiān)測中的應(yīng)用[A];重慶市測繪學(xué)會第三屆優(yōu)秀論文評選獲獎?wù)撐聂?005-2006年度學(xué)術(shù)交流會論文選編[C];2008年

7 張鐵軍;;年度土地利用變更調(diào)查中遙感監(jiān)測圖斑提取方法淺析[A];福建省土地學(xué)會2012年年會論文集[C];2012年

8 柳思聰;杜培軍;;基于形態(tài)學(xué)濾波的多時相遙感影像變化檢測方法研究[A];第十七屆中國遙感大會摘要集[C];2010年

9 劉志剛;李夕海;錢昌松;;遙感圖像變化檢測問題淺析[A];陜西地球物理文集（五）國家安全與軍事地球物理研究[C];2005年

10 劉翔;李萬茂;高連如;陶發(fā)達;倪金生;;基于遙感圖像變化檢測的投資項目搜索技術(shù)研究[A];中國遙感應(yīng)用協(xié)會2010年會暨區(qū)域遙感發(fā)展與產(chǎn)業(yè)高層論壇論文集[C];2010年

相關(guān)重要報紙文章 前1條

1 耿菲菲;影像數(shù)據(jù)分析:新行當?shù)拇鬂摿N];中國攝影報;2014年

相關(guān)博士學(xué)位論文 前10條

1 張海濤;基于高分影像的滑坡提取關(guān)鍵技術(shù)研究[D];中國地質(zhì)大學(xué);2017年

2 郝明;基于空間信息準確性增強的遙感影像變化檢測方法研究[D];中國礦業(yè)大學(xué);2015年

3 王凌霞;基于多尺度分析和自然進化優(yōu)化的遙感圖像配準與變化檢測[D];西安電子科技大學(xué);2015年

4 劉趕超;基于雙噪聲相似性模型的SAR圖像變化檢測[D];西安電子科技大學(xué);2016年

5 呂志勇;顧及影像空間特征的地表覆蓋變化檢測方法研究[D];武漢大學(xué);2014年

6 陸苗;面向地表覆蓋數(shù)據(jù)更新的變化檢測方法研究[D];武漢大學(xué);2014年

7 徐金燕;全極化星載SAR影像輻射校正與城區(qū)變化檢測[D];武漢大學(xué);2014年

8 武辰;遙感影像多層次信息變化檢測研究[D];武漢大學(xué);2015年

9 蘇昂;無人機圖像中的典型目標檢測與變化檢測方法研究[D];國防科學(xué)技術(shù)大學(xué);2016年

10 李向軍;遙感土地利用變化檢測方法探討[D];中國科學(xué)院研究生院（遙感應(yīng)用研究所）;2006年

相關(guān)碩士學(xué)位論文 前10條

1 容麗娜;基于面向?qū)ο蠓诸惖母叻直媛蔬b感影像變化檢測研究[D];西安科技大學(xué);2017年

2 張家琦;遙感影像變化檢測方法及應(yīng)用研究[D];中國地質(zhì)大學(xué)(北京);2015年

3 李玲玲;基于NSCT和NSST的圖像變化檢測算法研究及應(yīng)用[D];新疆大學(xué);2015年

4 胡雪梅;基于MST的遙感圖像變化檢測研究[D];新疆大學(xué);2015年

5 楊國棟;基于分布式并行聚類的SAR圖像變化檢測算法研究[D];西安電子科技大學(xué);2014年

6 王橋;基于多目標模糊聚類的SAR圖像變化檢測[D];西安電子科技大學(xué);2014年

7 張文婷;基于自適應(yīng)權(quán)值差異圖融合和聚類的SAR圖像變化檢測方法研究[D];西安電子科技大學(xué);2014年

8 任新營;基于OpenCL的并行SAR圖像變化檢測[D];西安電子科技大學(xué);2014年

9 宮金杞;面向地理國情監(jiān)測的地表覆蓋變化檢測方法研究及系統(tǒng)實現(xiàn)[D];山東農(nóng)業(yè)大學(xué);2015年

10 趙姣姣;基于無監(jiān)督方法的SAR圖像變化檢測[D];西安電子科技大學(xué);2014年

，

本文編號：2055882