Computing cluster technology is widely applied in remote sensing data parallel processing. This article proposes that introducing GeoSOT earth subdivision grid into computing cluster, to develop a method of remote sensing data parallel processing based on subdivision grid. The article elucidates GeoSOT grid and its coding at first, analyzes the advantages of GeoSOT grid in remote sensing data parallel processing; then introduces subdivision computing cluster architecture and multilevel nature of computing scheduling method based on subdivision code; then introduces load balancing control method of multilevel nature of subdivision computing cluster which based on subdivision code, and takes two cases, target detection parallel processing and...
AbstractThis research is satisfied with the needs of remote sensing massive data real time and fast ...
Since the traditional approaches of managing remote sensing image data could no longer deal with the...
The High-Performance and Disruptive Computing in Remote Sensing (HDCRS) Working Group (WG) was recen...
Remote sensing images have been widely used in various fields, how to process remote sensing images ...
In order to solve some problems in managing and parallel processing of global expanding remote sensi...
With advances in remote-sensing technology, the large volumes of data cannot be analyzed efficiently...
At present, there are various data grids to organize data in different department data centers. Inor...
Remote Sensing (RS) data processing is characterized by massive remote sensing images and increasing...
Remote Sensing (RS) data processing is characterized by massive remote sensing images and increasing...
This paper presents the method of improving the efficiency of information extraction based on featur...
The demand for parallel geocomputation based on raster data is constantly increasing with the increa...
Massive remote sensing image fast processing is one of the important tasks for remote sensing image ...
Owing to the rapid development of earth observation technology, the volume of spatial information is...
The discipline of remote sensing is concerned with observing the earth's suface using different port...
Massive remote sensing image fast processing is one of the important tasks for remote sensing image ...
AbstractThis research is satisfied with the needs of remote sensing massive data real time and fast ...
Since the traditional approaches of managing remote sensing image data could no longer deal with the...
The High-Performance and Disruptive Computing in Remote Sensing (HDCRS) Working Group (WG) was recen...
Remote sensing images have been widely used in various fields, how to process remote sensing images ...
In order to solve some problems in managing and parallel processing of global expanding remote sensi...
With advances in remote-sensing technology, the large volumes of data cannot be analyzed efficiently...
At present, there are various data grids to organize data in different department data centers. Inor...
Remote Sensing (RS) data processing is characterized by massive remote sensing images and increasing...
Remote Sensing (RS) data processing is characterized by massive remote sensing images and increasing...
This paper presents the method of improving the efficiency of information extraction based on featur...
The demand for parallel geocomputation based on raster data is constantly increasing with the increa...
Massive remote sensing image fast processing is one of the important tasks for remote sensing image ...
Owing to the rapid development of earth observation technology, the volume of spatial information is...
The discipline of remote sensing is concerned with observing the earth's suface using different port...
Massive remote sensing image fast processing is one of the important tasks for remote sensing image ...
AbstractThis research is satisfied with the needs of remote sensing massive data real time and fast ...
Since the traditional approaches of managing remote sensing image data could no longer deal with the...
The High-Performance and Disruptive Computing in Remote Sensing (HDCRS) Working Group (WG) was recen...