Research on Solid State Storage Based Remote Sensing Data Storage

2012 ◽  
Author(s):  
HongShan Nie ◽  
QiYou Xie ◽  
YuMei Zhang ◽  
Miao Li ◽  
Qiang Liu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Solid State ◽  
Data Storage ◽  
Remote Sensing Data ◽  
Sensing Data

scholarly journals Multi-channel data storage format definition for visualization tasks on the example of SPOT-4 images

2019 ◽  
pp. 302-308
Author(s):  
N Yu Sevastianova ◽  
N S Vinogradova
Keyword(s):  
Remote Sensing ◽  
Data Storage ◽  
Remote Sensing Data ◽  
Disk Arrays ◽  
Sensing Data ◽  
Incomplete Knowledge ◽  
Emergency Situations ◽  
Data Interleaving ◽  
Storage Format ◽  
Production Problems

One of the features of a remote sensing data storage is the widespread utilization of large-capacity disk arrays. Emergency situations arising from the use of arrays can lead to the fact that the remote sensing data, usually stored in uncompressed form, may become partially damaged. But even with incomplete recovery, this kind of data can be used in the future to solve production problems. However, this recovery is sometimes hampered by incomplete knowledge of the format of the corrupted data. The article describes an approach to automatic recognition of multichannel data interleaving type (BIP, BIL or BSQ) and its application to a recovery of SPOT-4 remote sensing data stored in the segment format "SEG", which were damaged after a disk array failure.

scholarly journals RESEARCH ON MULTI-SOURCE SATELLITE IMAGE DATABASE MANAGEMENT SYSTEM

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 565-568
Author(s):  
N. Fu ◽  
L. Sun ◽  
H. Z. Yang ◽  
J. Ma ◽  
B. Q. Liao
Keyword(s):  
Remote Sensing ◽  
Data Storage ◽  
Management System ◽  
Satellite Image ◽  
Remote Sensing Data ◽  
Database Management System ◽  
Sensor Data ◽  
Remote Sensing Images ◽  
Sensing Data ◽  
Effective Organization

Abstract. For the exploration and analysis of electricity, it is necessary to continuously acquire multi-star source, multi-temporal, multi-level remote sensing images for analysis and interpretation. Since the overall data has a variety of features, a data structure for multi-sensor data storage is proposed. On the basis of solving key technologies such as real-time image processing and analysis and remote sensing image normalization processing, the .xml file and remote sensing data geographic information file are used to realize effective organization between remote sensing data and remote sensing data. Based on GDAL design relational database, the formation of a relatively complete management system of data management, shared publishing and application services will maximize the potential value of remote sensing images in electricity remote sensing.

Review of data storage and management technologies for massive remote sensing data

2011 ◽  
Vol 54 (12) ◽  
pp. 3220-3232 ◽  
Author(s):  
XueFeng Lü ◽  
ChengQi Cheng ◽  
JianYa Gong ◽  
Li Guan
Keyword(s):  
Remote Sensing ◽  
Data Storage ◽  
Remote Sensing Data ◽  
Sensing Data

Research on Improved Method of Storage and Query of Large-Scale Remote Sensing Images

2018 ◽  
Vol 29 (3) ◽  
pp. 1-16 ◽  
Author(s):  
Jing Weipeng ◽  
Tian Dongxue ◽  
Chen Guangsheng ◽  
Li Yiyuan
Keyword(s):  
Remote Sensing ◽  
Data Storage ◽  
Large Scale ◽  
Remote Sensing Data ◽  
Index Structure ◽  
Hilbert Curve ◽  
Remote Sensing Images ◽  
Storage Model ◽  
Sensing Data ◽  
Low Efficiency

The traditional method is used to deal with massive remote sensing data stored in low efficiency and poor scalability. This article presents a parallel processing method based on MapReduce and HBase. The filling of remote sensing images by the Hilbert curve makes the MapReduce method construct pyramids in parallel to reduce network communication between nodes. Then, the authors design a massive remote sensing data storage model composed of metadata storage model, index structure and filter column family. Finally, this article uses MapReduce frameworks to realize pyramid construction, storage and query of remote sensing data. The experimental results show that this method can effectively improve the speed of data writing and querying, and has good scalability.

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.

Assessment of the national market demand for high-resolution remote sensing data

2002 ◽  
Vol 8 (1) ◽  
pp. 15-22
Author(s):  
V.N. Astapenko ◽  
◽  
Ye.I. Bushuev ◽  
V.P. Zubko ◽  
V.I. Ivanov ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Remote Sensing Data ◽  
Market Demand ◽  
Sensing Data ◽  
National Market
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