本文選題：遙感圖像處理　切入點(diǎn)：K-means算法　出處：《南京郵電大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：隨著科技的發(fā)展,特別是計(jì)算機(jī)技術(shù)和航空航天以及傳感技術(shù)的進(jìn)步,遙感技術(shù)應(yīng)運(yùn)而生,成為了管理和利用國土資源的一項(xiàng)最重要的技術(shù)手段。遙感技術(shù)的實(shí)時(shí)性、高效性、周期性短優(yōu)勢,讓其成為獲取國土資源觀測數(shù)據(jù)的一種重要的渠道。而針對(duì)于國土資源的動(dòng)態(tài)監(jiān)測成為了當(dāng)今遙感數(shù)據(jù)應(yīng)用的最主要的領(lǐng)域,也是科研工作的熱點(diǎn)之一。由于現(xiàn)代遙感技術(shù)能夠更快更方便的獲取大量的遙感圖像信息,傳統(tǒng)的手工監(jiān)測已經(jīng)不滿足當(dāng)前的技術(shù)要求,隨著計(jì)算機(jī)技術(shù)的發(fā)展,各種針對(duì)于遙感圖像的分類技術(shù)大量出現(xiàn),成為遙感圖像數(shù)據(jù)處理的主要手段。本文重點(diǎn)研究動(dòng)態(tài)K-means算法在遙感圖像挖掘領(lǐng)域的并行化計(jì)算,即結(jié)合BP神經(jīng)網(wǎng)絡(luò)算法通過動(dòng)態(tài)的分裂合并迭代過程最終確定聚類結(jié)果,并且針對(duì)衛(wèi)星遙感圖像進(jìn)行聚類處理,然后利用Hadoop實(shí)現(xiàn)聚類算法的并行化.。主要的研究內(nèi)容和創(chuàng)新點(diǎn)總結(jié)如下所述:(1)針對(duì)于傳統(tǒng)K-means算法性能受聚類中心初始化過程的約束的缺點(diǎn),算法在每次迭代過程中結(jié)合分裂與合并步驟動(dòng)態(tài)的確定最終聚類中心。實(shí)驗(yàn)證明,改進(jìn)后的算法具有更快的收斂速度并且可以提高聚類精度。(2)結(jié)合BP神經(jīng)網(wǎng)絡(luò)算法優(yōu)化分裂與合并算法的權(quán)值,針對(duì)于每次迭代過程哪些簇該分裂、哪些簇該合并、哪些簇不變進(jìn)行劃分。(3)介紹了算法并行化的可行性和思路,并將改進(jìn)后的算法在Hadoop平臺(tái)上實(shí)現(xiàn)了并行化處理,實(shí)現(xiàn)了對(duì)于大量遙感信息數(shù)據(jù)的處理。最后通過實(shí)驗(yàn)驗(yàn)證了算法的可靠性和高效性,在Hadoop平臺(tái)上實(shí)現(xiàn)的并行化實(shí)驗(yàn),驗(yàn)證了并行化后算法處理遙感數(shù)據(jù)的效率明顯提高。
[Abstract]:With the development of science and technology, especially the progress of computer technology, aerospace technology and sensing technology, remote sensing technology has emerged as the times require, and has become one of the most important technical means to manage and utilize land and resources. Periodic short-term advantage makes it an important way to obtain land and resources observation data. Dynamic monitoring of land and resources has become the most important field of remote sensing data application. Because the modern remote sensing technology can obtain a large amount of remote sensing image information more quickly and conveniently, the traditional manual monitoring can not meet the current technical requirements, with the development of computer technology, A variety of classification techniques for remote sensing images have emerged and become the main means of remote sensing image data processing. This paper focuses on the parallel computing of dynamic K-means algorithm in remote sensing image mining field. Combining with BP neural network algorithm, the clustering results are finally determined by the dynamic splitting and merging iteration process, and the clustering processing is carried out for satellite remote sensing images. Then using Hadoop to realize the parallelization of clustering algorithm... The main research contents and innovations are summarized as follows: 1) aiming at the shortcomings of traditional K-means algorithm that the performance is constrained by the initialization process of clustering center. In each iteration process, the algorithm dynamically determines the final clustering center by combining the split and merge steps. The experimental results show that, The improved algorithm has faster convergence speed and can improve the clustering accuracy. It combines BP neural network algorithm to optimize the weight of split and merge algorithm, aiming at which clusters are split and which clusters should be merged in each iteration process. Which clusters are invariant for partitioning. (3) the feasibility and idea of parallelization are introduced, and the improved algorithm is implemented on Hadoop platform. Finally, the reliability and efficiency of the algorithm are verified by experiments. The parallelization experiment implemented on Hadoop platform verifies that the efficiency of the algorithm in processing remote sensing data is obviously improved after parallelization.
【學(xué)位授予單位】：南京郵電大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP751;TP311.13

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 何志學(xué);莊連英;斯慶巴拉;;基于Hadoop平臺(tái)的XML Twig查詢處理方法[J];北華航天工業(yè)學(xué)院學(xué)報(bào);2015年01期

2 蔡江云;;基于微軟云計(jì)算平臺(tái)的海量數(shù)據(jù)挖掘系統(tǒng)分析[J];才智;2015年05期

3 張利娜;張東芳;白亞彬;;基于GDAL的遙感影像快速讀取與顯示方法研究[J];西部資源;2014年01期

4 田秀霞;周耀君;畢忠勤;彭源;;基于Hadoop架構(gòu)的分布式計(jì)算和存儲(chǔ)技術(shù)及其應(yīng)用[J];上海電力學(xué)院學(xué)報(bào);2011年01期

5 張文明;吳江;袁小蛟;;基于密度和最近鄰的K-means文本聚類算法[J];計(jì)算機(jī)應(yīng)用;2010年07期

6 蔡滿軍;程曉燕;喬剛;;一種改進(jìn)BP網(wǎng)絡(luò)學(xué)習(xí)算法[J];計(jì)算機(jī)仿真;2009年07期

7 曾江源;;ISODATA算法的原理與實(shí)現(xiàn)[J];科技廣場;2009年07期

8 曾明;魏衍;;一種BP神經(jīng)網(wǎng)絡(luò)改進(jìn)算法的研究及應(yīng)用[J];微計(jì)算機(jī)信息;2009年18期

9 劉雨搏;金寧;;基于共軛梯度法的多層神經(jīng)網(wǎng)絡(luò)訓(xùn)練方法[J];信息技術(shù);2008年03期

10 劉經(jīng)南;邵連軍;張訓(xùn)械;;GNSS-R研究進(jìn)展及其關(guān)鍵技術(shù)[J];武漢大學(xué)學(xué)報(bào)(信息科學(xué)版);2007年11期

相關(guān)碩士學(xué)位論文 前4條

1 瞿卓;基于Hadoop2.0的數(shù)據(jù)挖掘算法并行化研究[D];廣東工業(yè)大學(xué);2015年

2 m云龍;云計(jì)算平臺(tái)下的數(shù)據(jù)挖掘研究[D];南京郵電大學(xué);2013年

3 張明輝;基于Hadoop的數(shù)據(jù)挖掘算法的分析與研究[D];昆明理工大學(xué);2012年

4 徐慶伶;基于半監(jiān)督學(xué)習(xí)的遙感圖像分類研究[D];陜西師范大學(xué);2010年

，

本文編號(hào)：1640687