Separating manual operation from remote sensing image processing procedure for high performance parallel computing

2007 ◽  
Author(s):  
Wenling Xuan ◽  
Zongjian Lin ◽  
Xiuwan Chen ◽  
Gang Zhao
Keyword(s):  
Remote Sensing ◽  
Image Processing ◽  
Parallel Computing ◽  
High Performance ◽  
Remote Sensing Image ◽  
Manual Operation ◽  
Processing Procedure ◽  
Remote Sensing Image Processing

Research and Performance Analysis on Parallel Computing of Remote Sensing Image Based on MPICH

2013 ◽  
Vol 680 ◽  
pp. 540-545
Author(s):  
Jun Li ◽  
Wei Feng Ma
Keyword(s):  
Remote Sensing ◽  
Image Processing ◽  
Parallel Computing ◽  
Cluster Computing ◽  
Single Mode ◽  
Remote Sensing Image ◽  
Distributed Parallel Computing ◽  
And Performance ◽  
Occurrence Matrix ◽  
Remote Sensing Image Processing

The traditional centralized single mode becomes a “bottleneck” of remote sensing image processing which cannot meet the needs of future remote sensing image processing development. Fortunately, the distributed parallel computing has provided a turning point to the quick calculation of remote sensing image. This paper presents the cluster computing environment based on the MPI, and advances a project of a parallelized design to the gray level co-occurrence matrix algorithm. Moreover, the experimental data, which is due to the parallelized algorithm running in the cluster, is recorded and analyzed in several respects such as different nodes, time, speedup, efficiency and so on. The analyzed result shows that parallel computing cluster based on MPICH can efficiently improve the speed of remote sensing image processing in the case of more complex algorithms. However, when the number of node increases, the consuming time decreases, and the efficiency will decrease at the same time. So, it is rather important to keep the balance between performance and efficiency. The nodes can not be continuously added into computing, when the consuming time can be accepted.

Research on Method for Massive Pixel-Level Remote Sensing Image Processing Based on Hadoop

2013 ◽  
Vol 333-335 ◽  
pp. 1224-1230 ◽  
Author(s):  
Xiao Yu Wang ◽  
Guo Qing Li ◽  
Wen Yang Yu ◽  
Quan Zou
Keyword(s):  
Remote Sensing ◽  
Image Processing ◽  
Parallel Computing ◽  
Remote Sensing Image ◽  
Image Point ◽  
Data Format ◽  
Text Format ◽  
Binary Format ◽  
Computing Performance ◽  
Remote Sensing Image Processing

Recently, global change research has reflected the great challenge of massive distributed remote sensing image processing. Faced with such challenge, massive pixel-level remote sensing image processing reconstruction based on Hadoop is proposed, which focuses on the support of data format and the design of paralle computing. In order to support a variety of formats of remote sensing images and simplify the process of data parse, the processing flow transforms the remote sensing image into image information in binary format, as well as metadata information in xml format. Compared with converting to text format, there are two advantages for this conversion, reducing the amount of data after converted and remaining metadata information. To avoid MapReduce parallel computing performance interference caused by the algorithmic complexity, remote sensing image point operation is selected to do research about the design of parallel computing. The experimental results show that the proposed method has good scalability in the distributed Hadoop environment, along with the changing of the data quantity.

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