本文選題：遙感影像存儲 + Hadoop��； 參考：《電子科技大學》2016年碩士論文

【摘要】：遙感技術(shù)的快速發(fā)展使得遙感影像數(shù)據(jù)的種類成倍增加,同時在數(shù)量級上呈現(xiàn)出幾何級數(shù)的增長。隨著遙感影像數(shù)據(jù)應用與業(yè)務(wù)需求不斷提高,給遙感影像的管理提出了更高的要求,高效存儲和快速檢索成為當前亟待解決的問題。而在互聯(lián)網(wǎng)技術(shù)不斷革新的今天,云存儲作為新型存儲模型為數(shù)據(jù)提供無限的存儲能力,應用此技術(shù)于海量遙感影像的管理和存儲,也是當今研究的熱點。而對于空間數(shù)據(jù)的快速檢索,離不開高效索引技術(shù)的支撐,研究高效的索引機制對于空間數(shù)據(jù)的檢索具有重要價值和意義。除了檢索性能方面的要求,遙感影像存儲管理的另一個技術(shù)指標就是檢索準確度,比如從海量遙感影像中精確查找出具有某一地物特征的影像數(shù)據(jù)。因此,基于語義特征的遙感影像檢索也是論文中的研究點�；谝陨蠁栴}的提出,本論文對遙感影像數(shù)據(jù)管理和檢索做了一系列研究。因此,論文的主要研究工作和成果包括:(1)設(shè)計不同解析方法提取所需遙感影像元數(shù)據(jù),并將其組成遙感影像數(shù)據(jù)管理系統(tǒng)的數(shù)據(jù)表,基于這些元數(shù)據(jù)對遙感影像實現(xiàn)多種方式的查詢和檢索。此外,遙感影像數(shù)據(jù)作為一種空間數(shù)據(jù),對其檢索性能的優(yōu)劣直接受索引機制的影響,因此本文還設(shè)計了實驗驗證不同空間索引機制的性能差異,即利用二維空間數(shù)據(jù)對建立的索引樹進行性能測試。該實驗基于不同的數(shù)據(jù)量級與索引項,通過Java程序以可視化的方式實現(xiàn)對不同索引技術(shù)的性能測試和驗證。(2)設(shè)計對遙感影像進行語義特征檢索的技術(shù)方案,基于該方案完成對遙感影像所包含的地物特征的提取,并將這些特征映射為一組語義ID的字符串,以該字符串作為遙感影像管理系統(tǒng)的元數(shù)據(jù)存入數(shù)據(jù)庫中。主要思想是:首先,基于分水嶺變換和Full Lambda-Schedule合并分塊算法,分割原始遙感影像;其次,基于SVM分類算法完成遙感影像特征提取和分類;最后將分類好的特征映射為語義ID。(3)開發(fā)遙感影像數(shù)據(jù)管理系統(tǒng)。通過對系統(tǒng)的需求分析,設(shè)計該系統(tǒng)的系統(tǒng)架構(gòu),由此架構(gòu)開發(fā)了基于Hadoop的遙感影像數(shù)據(jù)管理系統(tǒng)。重點研究了Hadoop系統(tǒng)的文件交互流程,并基于HDFS高效地存儲管理遙感影像數(shù)據(jù)。遙感影像數(shù)據(jù)管理系統(tǒng)是一個B/S架構(gòu)模型系統(tǒng),通過設(shè)計每層功能模塊,最終完成影像的查詢檢索、快速瀏覽以及本地下載等。
[Abstract]:The rapid development of remote sensing technology makes the kind of remote sensing image data multiplied, and at the same time it presents the growth of geometric progression. With the application of remote sensing image data and the increasing demand of business, the management of remote sensing image has been raised higher, and the efficient storage and fast retrieval have become the urgent problems to be solved. With the continuous innovation of Internet technology, the cloud storage, as a new storage model, provides unlimited storage capacity for data. The application of this technology to the management and storage of massive remote sensing images is also a hot topic in the present research. The retrieval of spatial data is of great value and significance. In addition to the requirements of the retrieval performance, the other technical index of remote sensing image storage management is the retrieval accuracy, such as the accurate search of the image data with a certain feature from the mass remote sensing images. Therefore, the remote sensing image retrieval based on the semantic features is also in the paper. Based on the above problems, this paper makes a series of research on remote sensing image data management and retrieval. Therefore, the main research work and achievements of this paper include: (1) design different analytical methods to extract the remote sensing image metadata needed for remote sensing image data and make up the data table of remote sensing image data management system, based on these metadata In addition, the remote sensing image data as a kind of spatial data is directly influenced by the index mechanism, so the performance difference of the different spatial index mechanism is also designed in this paper, that is, the performance measurement of the index tree based on the two-dimensional spatial data is used. This experiment is based on the different data magnitude and index terms, through the Java program to visualize the performance test and verification of different index techniques. (2) a technical scheme for semantic feature retrieval of remote sensing images is designed, which is based on the scheme to extract the features of the objects contained in the remote sensing images and reflect these features. As a string of semantic ID, the string is stored as the metadata of remote sensing image management system. The main idea is: first, based on watershed transform and Full Lambda-Schedule merging block algorithm, the original remote sensing image is segmented. Secondly, the feature extraction and classification of remote sensing images are completed based on the SVM classification algorithm; finally, the remote sensing image features are extracted and classified. The classified features are mapped to semantic ID. (3) development of remote sensing image data management system. Through the analysis of the system requirements, the system architecture is designed, and the remote sensing image data management system based on Hadoop is developed. The document interaction process of the Hadoop system is focused on, and the remote sensing image is efficiently stored and managed based on the HDFS. Like data. Remote sensing image data management system is a B/S architecture model system. Through the design of each functional module, the image query and retrieval, rapid browsing and local downloading are completed.

【學位授予單位】：電子科技大學
【學位級別】：碩士
【學位授予年份】：2016
【分類號】：TP751

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