| With the rapid development of remote sensing technology,the organization and management of mass remote sensing image data become more and more complicated.Remote sensing image metadata is the data describing remote sensing image data.How to organize and manage it efficiently is an important problem in remote sensing application research.Remote sensing image contains many kinds of target objects,which results in many metadata description items and dense tags.Due to the limitation of objective technical conditions,remote sensing images and processing results contain a lot of uncertain information,so the corresponding remote sensing metadata is generally described by fuzzy XML documents.With the continuous popularization of remote sensing information services,the storage and sharing of massive remote sensing metadata has become a bottleneck restricting the performance of remote sensing information processing system.At present,the remote sensing information processing system usually adopts compression technology to reduce the storage and transmission cost,but the query and analysis of remote sensing metadata need to be uncompressed,which brings extra performance cost.It is very urgent to study fuzzy XML document query algorithm without decompression.Since the query algorithm of compressed document needs to be carried out on the specific compressed data structure,the compression and query technology of fuzzy XML document of remote sensing metadata are a pair of correlation problems that need to be studied simultaneously.Aiming at the above problems,this paper first designs a fuzzy XML document compression algorithm which supports undecompressed query.On this basis,a keyword query algorithm which can directly query remote sensing metadata in fuzzy XML compressed document is proposed.The technology is further applied to the functional module of remote sensing metadata storage and transmission of the common application support platform of the national civil space infrastructure land observation satellite.The main work of this paper is as follows:1.This paper proposes a compression algorithm FAPEr algorithm for fuzzy XML documents.The algorithm re-encodes the repeated tags and attributes in fuzzy XML documents with short bytes to improve the compression rate.This algorithm is an improvement on the previous FAPE algorithm.The compression object of the FAPE algorithm is an exact XML document.In order to solve the problem that the original algorithm cannot process fuzzy XML documents,a method of processing fuzzy structures in fuzzy XML documents is added.A new method of Linked Hash Map is designed to reduce the process of node updating and improve the efficiency of the algorithm.Experiments show that the compression ratio of this algorithm is significant.2.A keyword query algorithm BIndex algorithm for fuzzy XML documents is proposed.This algorithm can query the keyword and return the corresponding result on the compressed document of FAPEr algorithm,which solves the problem that the traditional query algorithm cannot query the remote sensing metadata of compressed fuzzy XML document.The algorithm quickly queries SLCA nodes by constructing matching structures and adopts a top-down query strategy to ignore intermediate invalid results.The experiment shows that the query efficiency of the algorithm is greatly improved.3.A functional module of remote sensing metadata storage and transmission is designed and implemented.The function module can compress and query the fuzzy XML document of remote sensing metadata.The module is developed in Java,and an analytical.XML package is developed based on the Document class of XML Document processing by DOM4 j,a JAR package in Java.Contains the Main Windows class,Xml Utils class,and Standalone Writer class,and 11 methods.The package has been successfully applied in the common application support platform of national civil space infrastructure Land Observation satellite,which can successfully realize remote sensing metadata compression and keyword query. |
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