Remote sensing data parallel processing base on cloud platform

2011 ◽  
Author(s):  
Haitao Wei ◽  
Yunyan Du ◽  
Chunjin Zhang ◽  
Wang Xin
Keyword(s):  
Remote Sensing ◽  
Parallel Processing ◽  
Remote Sensing Data ◽  
Cloud Platform ◽  
Sensing Data ◽  
Data Parallel

A Universal Parallel Framework For Remote Sensing Process

2012 ◽  
Vol 239-240 ◽  
pp. 599-602
Author(s):  
Xing Wen Cai ◽  
Jian Hu ◽  
Zi Yang Li ◽  
Bo Zhu
Keyword(s):  
Remote Sensing ◽  
Parallel Processing ◽  
Message Passing ◽  
Message Passing Interface ◽  
Job Scheduling ◽  
Processing System ◽  
Remote Sensing Data ◽  
Parallel Job Scheduling ◽  
Sensing Data ◽  
Data Parallel

Parallel computing technology has been widely used to process massive remote sensing data high efficiently. In order to simplify the development of remote sensing data parallel processing system and consider about the characteristics of remote sensing data pre-processing, this paper designs a cluster-based universal parallel processing framework. The framework encapsulates parallel job scheduling and management, adapts the strategy of components development, provides the simple interface for the users to develop new functionalities by adding new data-processing components into the framework. Basing on Message Passing Interface (MPI), the framework is implemented. Experiments, such as adding remote sensing data extracting, radiometric correction and geometric correction into the framework, show that the framework performed well in computing efficiency and speedup rate.

scholarly journals STUDY AND ANALYSIS OF REMOTE SENSING DATA PARALLEL PROCESSING

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 443-450
Author(s):  
C. Y. Li ◽  
G. Q. Zhou ◽  
X. Zhou ◽  
D. Q. Liu
Keyword(s):  
Remote Sensing ◽  
Parallel Processing ◽  
Data Processing ◽  
Remote Sensing Data ◽  
Floating Point ◽  
Fpga Design ◽  
Sensing Data ◽  
Computational Performance ◽  
Calculation Module ◽  
Division Operation

Abstract. This paper analyzes a varieties of procedure of remote sensing data processing, and explores the common mathematical models, common algorithm models, and public function processing units of data processing shared by different tasks or even different parts within an individual task. Public modules are established to improve the parallelism of remote sensing data processing based on FPGA, which has excellent parallel processing performance. In addition, in order to reduce the resource consumption and increase the calculation efficiency of the designed FPGA program, the method of avoiding floating-point arithmetic and division operation in FPGA programming are discussed in this paper. There are a large number of common calculation modules between different tasks, such as the rotation matrix calculation module in attitude solution, geometric correction, and orthorectification task. Image preprocessing, feature information extraction, image threshold separation, and connected region markers are all common processing modules for a target detection task. In the same task, there is also a common calculation module. When using the FPGA design program, the power series of 2 can be used to convert the floating-point operation to fixed-point operation with an acceptable precision. A similar approach can transform the division operation into multiplication and shift operations, thereby improve the computational performance of FPGA programming.

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

A procedure for integrated assessment of landscape fire risk using remote sensing data

2011 ◽  
Vol 17 (6) ◽  
pp. 30-44
Author(s):  
Yu.V. Kostyuchenko ◽  
◽  
M.V. Yushchenko ◽  
I.M. Kopachevskyi ◽  
S. Levynsky ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Integrated Assessment ◽  
Remote Sensing Data ◽  
Fire Risk ◽  
Sensing Data
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