A new architecture paradigm for image processing pipeline applied to massive remote sensing data production

2018 ◽  
Author(s):  
Antoine Masse ◽  
Olivier Melet ◽  
Yannick Ott ◽  
Pierre Lassalle
Keyword(s):  
Remote Sensing ◽  
Image Processing ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
Processing Pipeline ◽  
Data Production

scholarly journals A cloud-based remote sensing data production system

2018 ◽  
Vol 86 ◽  
pp. 1154-1166 ◽  
Author(s):  
Jining Yan ◽  
Yan Ma ◽  
Lizhe Wang ◽  
Kim-Kwang Raymond Choo ◽  
Wei Jie
Keyword(s):  
Remote Sensing ◽  
Production System ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
Data Production

scholarly journals Infrastructures and services for remote sensing data production management across multiple satellite data centers

2016 ◽  
Vol 19 (3) ◽  
pp. 1243-1260 ◽  
Author(s):  
Jie Zhang ◽  
Jining Yan ◽  
Yan Ma ◽  
Dong Xu ◽  
Pengfei Li ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Satellite Data ◽  
Production Management ◽  
Data Centers ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
Data Production

scholarly journals Knowledge technologies based on fabrication process composite materials and remote sensing applications

2020 ◽  
Vol 29 ◽  
pp. 2633366X1989598
Author(s):  
Faten A Mustafa ◽  
Oguz Bayat
Keyword(s):  
Remote Sensing ◽  
Image Processing ◽  
Composite Materials ◽  
Water Level ◽  
Human Life ◽  
Remote Sensing Data ◽  
Dam Reservoir ◽  
Composite Image ◽  
Sensing Data ◽  
Hybrid Classification

The aim of this work concentrates on utilizing powerful MATLAB programming (software version R2016a) to evaluate the impact of environmental variations of water case in the Mosul Dam reservoir and observed its receding impact on human life activities based on composite image processing applications. Furthermore, composite materials of different temporal remote sensing data increase powerfully the estimation of environmental variables of relevance to human health. Thus, temporal remote sensing data trends to enhance the efficiency of detecting receding water resources effect of human life impacts over different years. Two steps were implemented, which focuses on the estimation of changes in the water surface of the lake over 31 years. Preprocessing step concentrates on composite data materials from different Landsats to be more suitable for next step by utilizing color composite image processing and postprocessing step implemented the coastline detection of the reservoir and recognition of the quality of clear water in the lake due to the variation of water spectral reflectivity by hybrid classification method. The performance of this study is based on statistics measurements on the surface area of water level and overall accuracy, which indicated that hybrid classification method improves the capacity of integrating two classification methods, which gained highly identification water lake classes regarding its quality and more. The obtained results achieved the desired purpose of this study to investigate the high power application through implementing composite different image processing techniques with temporal satellite data to conversance the amount of water level changes in Mosul Dam reservoir and its impact on storage quantity over years.

scholarly journals A HADOOP-BASED DISTRIBUTED FRAMEWORK FOR EFFICIENT MANAGING AND PROCESSING BIG REMOTE SENSING IMAGES

2015 ◽  
Vol II-4/W2 ◽  
pp. 63-66 ◽  
Author(s):  
C. Wang ◽  
F. Hu ◽  
X. Hu ◽  
S. Zhao ◽  
W. Wen ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Image Processing ◽  
Big Data ◽  
Remote Sensing Data ◽  
Distributed File System ◽  
Computing Environment ◽  
Remote Sensing Images ◽  
Sensing Data ◽  
Distributed Framework ◽  
Hadoop Distributed File System

Various sensors from airborne and satellite platforms are producing large volumes of remote sensing images for mapping, environmental monitoring, disaster management, military intelligence, and others. However, it is challenging to efficiently storage, query and process such big data due to the data- and computing- intensive issues. In this paper, a Hadoop-based framework is proposed to manage and process the big remote sensing data in a distributed and parallel manner. Especially, remote sensing data can be directly fetched from other data platforms into the Hadoop Distributed File System (HDFS). The Orfeo toolbox, a ready-to-use tool for large image processing, is integrated into MapReduce to provide affluent image processing operations. With the integration of HDFS, Orfeo toolbox and MapReduce, these remote sensing images can be directly processed in parallel in a scalable computing environment. The experiment results show that the proposed framework can efficiently manage and process such big remote sensing data.

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