Design And Implementation Of Remote Sensing Image Data Storage And Retrieval Model Based On Geotrellis

In view of the characteristics of multi-scale,multi-temporal,global coverage and high resolution of remote sensing data,the amount of data of remote sensing data has exploded.This increases the difficulty of storing and retrieving remote sensing data.Because of the different formats of remote sensing data,large amount of file data,complicated processing,large variety of remote sensing data,difficulty in displaying and releasing remote sensing images,remote sensing images Data is greatly hindered in various aspects such as transmission,storage,management,data sharing,data retrieval,data processing,and data distribution.Especially in remote sensing storage and retrieval,it faces problems such as low storage efficiency and slow data retrieval.The storage and retrieval of remote sensing images has become a major bottleneck restricting the application of remote sensing and an urgent need for ground observation application technology.This paper studies the design and implementation of a geotrellis remote sensing image data storage and retrieval model for the problem of massive,heterogeneous and multi-source remote sensing data that is difficult to store and retrieve quickly.In general,the main work of the paper includes the following aspects:(1)This paper adopts the Raster Frame grid framework,which provides the powerful geographic function of Spark DataFrames,and provides technical information support from the slice layer of Geotrellis.The Spark Catalyst data type flexibility and ease of use are used to process the analysis space-time grid.Data,using the user-defined type TileUDF to encode Geotrellis with the Spark Catalyst engine to generate inbound metadata information for GeoTrellis Layers.(2)Based on the Geotrellis geoprocessing framework tool,this paper realizes the structured display of remote sensing image metadata,the rapid storage of remote sensing images,and the parallel construction of remote sensing image pyramids.The geortrelis geographic data storage framework enables one-stop storage of remote sensing data,greatly improving the storage efficiency of remote sensing data.The distributed database Accumulo,Hbase and distributed file system HDFS are used as the back end of the Geotrellis geographic data framework,enriching the diversity of the storage framework.The image pyramid is constructed by using the index methodof three spatial filling curves.By using the different parameters of Geotrellis to construct the image pyramid,the purpose of storing the remote sensing image efficiently is achieved.(3)This paper proposes a distributed storage and query scheme for remote sensing images in the storage end HBase of Geotrellis in the geographic big data framework.This solution quickly segmentation of remote sensing images and designs an indexing solution based on slice ID and attribute data based on segmented images.Then,by using HBase's filtering mechanism to design the filter column family,the purpose of filtering data during query is achieved.(4)This paper uses the open source raster data conversion library(GDAL)and cache WMS(Web Map Service)Tile open source project(GeoWebCache)for raster image slicing,the slice is stored in the back end of Geotrellis,using Akka multi-process The access mechanism publishes the remote sensing data of the tile through the leaflet open source library of Leaflet to publish the WMS map service.The tile data can also be loaded into the GeoWebCache slice image data through the OpenLayers front end,and then the tile image data is loaded by Cesium to visualize the remote sensing data.(5)Through the comparison of different platforms,different spatial indexing techniques and remote sensing image pyramid storage of different file systems,it can be seen that the storage model based on Geotrellis remote sensing image is more efficient and more suitable for index creation of remote sensing image metadata.This paper compares the query efficiency of different index tile data.This paper adopts the HBase-based secondary indexing mechanism.When the query ratio is not high,the query efficiency is higher than that of the Z-Order spatial index.In the case where there are few query items,the tile data is efficiently searched more efficiently.