scholarly journals ScienceEarth: A Big Data Platform for Remote Sensing Data Processing

2020 ◽  
Vol 12 (4) ◽  
pp. 607 ◽  
Author(s):  
Chen Xu ◽  
Xiaoping Du ◽  
Zhenzhen Yan ◽  
Xiangtao Fan
Keyword(s):  
Remote Sensing ◽  
Data Processing ◽  
Large Scale ◽  
Cluster Computing ◽  
Remote Sensing Data ◽  
Data Retrieval ◽  
Hilbert Curve ◽  
Sensing Data ◽  
System A ◽  
Efficient Data

Mass remote sensing data management and processing is currently one of the most important topics. In this study, we introduce ScienceEarth, a cluster-based data processing framework. The aim of ScienceEarth is to store, manage, and process large-scale remote sensing data in a cloud-based cluster-computing environment. The platform consists of the following three main parts: ScienceGeoData, ScienceGeoIndex, and ScienceGeoSpark. ScienceGeoData stores and manages remote sensing data. ScienceGeoIndex is an index and query system, a spatial index based on quad-tree and Hilbert curve which is combined for heterogeneous tiled remote sensing data that makes efficient data retrieval in ScienceGeoData. ScienceGeoSpark is an easy-to-use computing framework in which we use Apache Spark as the analytics engine for big remote sensing data processing. The result of tests proves that ScienceEarth can efficiently store, retrieve, and process remote sensing data. The results reveal ScienceEarth has the potential and capabilities of efficient big remote sensing data processing.

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.

Improved t-SNE based manifold dimensional reduction for remote sensing data processing

2018 ◽  
Vol 78 (4) ◽  
pp. 4311-4326 ◽  
Author(s):  
Weijing Song ◽  
Lizhe Wang ◽  
Peng Liu ◽  
Kim-Kwang Raymond Choo
Keyword(s):  
Remote Sensing ◽  
Data Processing ◽  
Dimensional Reduction ◽  
Remote Sensing Data ◽  
Sensing Data

LESS: LargE-Scale remote sensing data and image simulation framework over heterogeneous 3D scenes

2019 ◽  
Vol 221 ◽  
pp. 695-706 ◽  
Author(s):  
Jianbo Qi ◽  
Donghui Xie ◽  
Tiangang Yin ◽  
Guangjian Yan ◽  
Jean-Philippe Gastellu-Etchegorry ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Large Scale ◽  
Remote Sensing Data ◽  
Image Simulation ◽  
Simulation Framework ◽  
Sensing Data
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