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Distributed retrieval for massive remote sensing image metadata on spark
- Source :
- IGARSS
- Publication Year :
- 2016
- Publisher :
- IEEE, 2016.
-
Abstract
- The massive data is constantly and rapidly growing in remote sensing field. How to achieve efficient storage and rapid retrieval of massive remote sensing image metadata is a difficult problem. Big Data technologies provide convenient and fast tools for the storage, retrieval and analysis of remote sensing image metadata. According to the feature of remote sensing image metadata, we first give new concepts of fat grid and thin grid and propose a grid indexing method called Spark-Fat-Thin-Grid-Index (SFTGridIndex). In SFTGridIndex, the index file and partitions files are stored in HDFS. We then design an optimized retrieval method based on SFTGrid-Index. The method can avoid the intersection computing of a large number of polygons. This research can ensure efficient storage and fast query of massive remote sensing image metadata and has good scalability.
- Subjects :
- Data element
Remote sensing application
Computer science
business.industry
Search engine indexing
Big data
0211 other engineering and technologies
02 engineering and technology
Grid
Metadata
Semantic grid
Scalability
Spark (mathematics)
Data_FILES
0202 electrical engineering, electronic engineering, information engineering
020201 artificial intelligence & image processing
business
021101 geological & geomatics engineering
Remote sensing
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)
- Accession number :
- edsair.doi...........1399e2a23d210b83727760f7402d4808