本文選題：遙感圖像 + 數(shù)據(jù)挖掘　； 參考：《東北大學(xué)》2014年碩士論文

【摘要】：遙感圖像數(shù)據(jù)挖掘是一個(gè)有著廣闊應(yīng)用前景的研究領(lǐng)域。由于遙感圖像數(shù)據(jù)庫的海量特征,遙感圖像數(shù)據(jù)挖掘已成為空間數(shù)據(jù)挖掘的主流。近年來,隨著圖像獲取和圖像存儲(chǔ)技術(shù)的迅速發(fā)展,使得人們能夠較為方便地得到大量有用的遙感圖像數(shù)據(jù)。圖像數(shù)據(jù)挖掘是用來挖掘圖像數(shù)據(jù)中隱含的知識(shí)、圖像內(nèi)或圖像間的各種關(guān)系以及其他隱藏在圖像數(shù)據(jù)中的各種模式的一種技術(shù),目前仍處于實(shí)驗(yàn)研究階段,是一個(gè)新興的、但極有發(fā)展?jié)摿Φ难芯款I(lǐng)域。其中一類方法是通過衛(wèi)星收集數(shù)據(jù),并通過Apriori等基本算法以及系列算法,挖掘出不同對象不同屬性間的關(guān)聯(lián)規(guī)則。這意味著序列模式挖掘算法可以集成到遙感圖像數(shù)據(jù)挖掘算法之中。作為遙感圖像數(shù)據(jù)挖掘方法的核心,序列模式挖掘算法的性能一直是影響方法性能的瓶頸。由于Apriori算法、PrefixSpan算法在挖掘大數(shù)據(jù)集上的劣勢,針對遙感圖像數(shù)據(jù)集,本文提出了基于BIDE的遙感圖像數(shù)據(jù)挖掘方法,并對其中的閉序列模式挖掘算法進(jìn)行了深入的研究與改進(jìn),使之能夠更好的挖掘遙感圖像數(shù)據(jù)集。本文把BIDE算法集成到遙感圖像數(shù)據(jù)挖掘方法中。這種閉序列模式挖掘算法不需要維護(hù)候選閉序列,可以直接進(jìn)行閉序列檢查,并且可以快速完成搜索空間削減。針對遙感圖像數(shù)據(jù)集,本文對算法的各個(gè)模塊進(jìn)行了測試,證明了方法的有效性、高效性。對于更大規(guī)模的遙感圖像數(shù)據(jù)集,BIDE算法在閉序列檢查和搜索空間削減的過程中需要進(jìn)行大量字符匹配和支持度計(jì)算操作。這兩種操作產(chǎn)生了大量的時(shí)間開銷。針對其弱點(diǎn),本文提出一種基于位置擴(kuò)展的閉序列模式挖掘算法—CSBIDEP算法,通過記錄每個(gè)事件的位置信息,利用位置信息得到頻繁1-序列,并對其進(jìn)行直接位置擴(kuò)展驗(yàn)證,以減少對投影數(shù)據(jù)庫的掃描,節(jié)省時(shí)間的開銷。針對不同規(guī)模的數(shù)據(jù)集,本文將基于位置擴(kuò)展的閉序列模式挖掘算法與BIDE算法進(jìn)行了比較實(shí)驗(yàn)。從實(shí)驗(yàn)結(jié)果看出,前者的時(shí)間性能有了顯著地提高。
[Abstract]:Remote sensing image data mining is a promising research field. Because of the massive features of remote sensing image database, remote sensing image data mining has become the mainstream of spatial data mining. In recent years, with the rapid development of image acquisition and image storage technology, people can easily get a large number of useful remote sensing image data. Image data mining is a technique used to mine hidden knowledge in image data, relationships within and between images, and other patterns hidden in image data. It is still in the stage of experimental research and is a new technology. But there is great potential for research. One kind of method is collecting data by satellite, mining association rules between different objects and attributes by using basic algorithms such as Apriori and a series of algorithms. This means that sequential pattern mining algorithm can be integrated into remote sensing image data mining algorithm. As the core of remote sensing image data mining, the performance of sequential pattern mining is always the bottleneck. Because of the disadvantage of Apriori algorithm PrefixSpan algorithm in mining big data sets, this paper proposes a method of remote sensing image data mining based on BIDE, and makes a deep research and improvement on the closed sequence pattern mining algorithm. So that it can better mining remote sensing image data sets. In this paper, the BIDE algorithm is integrated into the remote sensing image data mining method. This closed sequence pattern mining algorithm does not need to maintain candidate closed sequences, it can directly check the closed sequences, and can quickly complete the search space reduction. Based on the remote sensing image data set, the algorithm modules are tested in this paper, and the validity and efficiency of the method are proved. For the larger remote sensing image data set Bide algorithm, a large number of character matching and support calculation operations are needed in the process of closed sequence checking and searching space reduction. These two operations have a lot of time overhead. Aiming at its weakness, this paper proposes a closed sequence pattern mining algorithm based on location expansion-CSBIDEP algorithm. By recording the location information of each event, the frequent 1- sequence can be obtained by using the location information, and it is verified by direct location expansion. To reduce the scan of the projection database, saving time and overhead. In this paper, the closed sequence pattern mining algorithm based on location expansion is compared with the BIDE algorithm for different data sets. The experimental results show that the time performance of the former has been improved significantly.
【學(xué)位授予單位】：東北大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TP311.13;TP751

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