GRIMS: A Scalable Management and Storage System for Massive Remote Sensing Images

2008 ◽  
Author(s):  
Liang ZHAO ◽  
Luo CHEN ◽  
Ning JING ◽  
Huaiyu ZUO
Keyword(s):  
Remote Sensing ◽  
Storage System ◽  
Remote Sensing Images ◽  
And Storage

scholarly journals RSIMS: Large-Scale Heterogeneous Remote Sensing Images Management System

2021 ◽  
Vol 13 (9) ◽  
pp. 1815
Author(s):  
Xiaohua Zhou ◽  
Xuezhi Wang ◽  
Yuanchun Zhou ◽  
Qinghui Lin ◽  
Jianghua Zhao ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Data Storage ◽  
Management System ◽  
Large Scale ◽  
Distributed Storage ◽  
Storage System ◽  
Remote Sensing Data ◽  
Data Access ◽  
Remote Sensing Images ◽  
Sensing Data

With the remarkable development and progress of earth-observation techniques, remote sensing data keep growing rapidly and their volume has reached exabyte scale. However, it's still a big challenge to manage and process such huge amounts of remote sensing data with complex and diverse structures. This paper designs and realizes a distributed storage system for large-scale remote sensing data storage, access, and retrieval, called RSIMS (remote sensing images management system), which is composed of three sub-modules: RSIAPI, RSIMeta, RSIData. Structured text metadata of different remote sensing images are all stored in RSIMeta based on a set of uniform models, and then indexed by the distributed multi-level Hilbert grids for high spatiotemporal retrieval performance. Unstructured binary image files are stored in RSIData, which provides large scalable storage capacity and efficient GDAL (Geospatial Data Abstraction Library) compatible I/O interfaces. Popular GIS software and tools (e.g., QGIS, ArcGIS, rasterio) can access data stored in RSIData directly. RSIAPI provides users a set of uniform interfaces for data access and retrieval, hiding the complex inner structures of RSIMS. The test results show that RSIMS can store and manage large amounts of remote sensing images from various sources with high and stable performance, and is easy to deploy and use.

OPTIMAL OPERATION POINT IN 3D DCT- BASED LOSSY COMPRESSION OF COLOR AND MULTICHANNEL REMOTE SENSING IMAGES

2015 ◽  
Vol 74 (20) ◽  
pp. 1803-1821 ◽  
Author(s):  
V. V. Lukin ◽  
S. K. Abramov ◽  
R.A. Kozhemiakin ◽  
Benoit Vozel ◽  
B. Djurovic ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Lossy Compression ◽  
Optimal Operation ◽  
Remote Sensing Images

A Survey of Automatic Road Extraction from Remote Sensing Images

2010 ◽  
Vol 36 (7) ◽  
pp. 912-922 ◽  
Author(s):  
Liang WU ◽  
Yun-An HU
Keyword(s):  
Remote Sensing ◽  
Road Extraction ◽  
Remote Sensing Images

Design and Development of City Application Service Platform for Multi-source Remote Sensing Images

2012 ◽  
Vol 14 (1) ◽  
pp. 116-122
Author(s):  
Jiusheng ZHONG ◽  
Nan JIANG ◽  
Bin HU ◽  
Junshu WANG
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Images ◽  
Design And Development ◽  
Service Platform ◽  
Application Service

Fuzzy segmentation of urban areas in panchromatic remote sensing images

2009 ◽  
Vol 28 (12) ◽  
pp. 3112-3115
Author(s):  
Yan CHEN ◽  
Shou-hong WAN ◽  
Yu-chang GONG
Keyword(s):  
Remote Sensing ◽  
Urban Areas ◽  
Remote Sensing Images ◽  
Fuzzy Segmentation
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