Satellite image processing on computational grids

Remote sensing image processing is a very demanding procedure in terms of data manipulation and computing power. Grid computing is a possible solution when the required computing performance or data sharing is not available at the user's site. Two scenarios of using Service Grids were analyzed in our papers [17, 18]. This paper discusses another scenario of using Computational Grids. According to this scenario a prototype code for satellite image classification was designed, implemented and tested.

[1]  Thomas S. Pagano,et al.  Moderate Resolution Imaging Spectroradiometer (MODIS) , 1993, Defense, Security, and Sensing.

[2]  Carl Kesselman,et al.  Near-real-time satellite image processing: metacomputing in CC++ , 1996, IEEE Computer Graphics and Applications.

[3]  Alois Goller,et al.  Method execution on a distributed image processing back-end , 1998, Proceedings of the Sixth Euromicro Workshop on Parallel and Distributed Processing - PDP '98 -.

[4]  Developing a Distributed Image Processing and Management Framework , 2000 .

[5]  Tony Pan,et al.  Image processing for the grid: a toolkit for building grid-enabled image processing applications , 2003, CCGrid 2003. 3rd IEEE/ACM International Symposium on Cluster Computing and the Grid, 2003. Proceedings..

[6]  Andrew L. Wendelborn,et al.  The PAGIS Grid Application Environment , 2003, International Conference on Computational Science.

[7]  Yong Meng Teo,et al.  Distributed geo-rectification of satellite images using Grid computing , 2003, Proceedings International Parallel and Distributed Processing Symposium.

[8]  Massimo Cafaro,et al.  A dynamic earth observation system , 2003, Parallel Comput..

[9]  Luigi Fusco,et al.  Grid technology for the storage and processing of remote sensing data: description of an application , 2003, SPIE Remote Sensing.

[10]  Wilson Rivera,et al.  Grid-HSI: Using grid computing to enable hyperspectral imaging analysis , 2004, Communications, Internet, and Information Technology.

[11]  Ying Luo,et al.  Preliminary Study on Unsupervised Classification of Remotely Sensed Images on the Grid , 2004, International Conference on Computational Science.

[12]  Geoffrey C. Fox,et al.  Messaging in web service grid with applications to geographical information systems , 2004, High Performance Computing Workshop.

[13]  Hai Jin,et al.  Use Case Study of Grid Computing with CGSP , 2005, Human.Society@Internet.

[14]  Yan Ren,et al.  The Architecture of SIG Computing Environment and Its Application to Image Processing , 2005, GCC.

[15]  Dorian Gorgan,et al.  Satellite Image Processing Applications in MedioGRID , 2006, 2006 Fifth International Symposium on Parallel and Distributed Computing.

[16]  Hartmut Rosengarten TerraShare – Distributed Image Data Management , 2006 .

[17]  Heinz Stockinger,et al.  Defining the grid: a snapshot on the current view , 2007, The Journal of Supercomputing.

[18]  Floricica Parauan,et al.  Clouds Mask Algorithm , 2006, 2006 Eighth International Symposium on Symbolic and Numeric Algorithms for Scientific Computing.

[19]  Adrian Colesa,et al.  Providing High Data Availability in MedioGRID , 2006, 2006 Eighth International Symposium on Symbolic and Numeric Algorithms for Scientific Computing.

[20]  Dorian Gorgan,et al.  MODIS Image Based Computation of Vegetation Indices in MedioGRID Architecture , 2006, 2006 Eighth International Symposium on Symbolic and Numeric Algorithms for Scientific Computing.

[21]  Dana Petcu,et al.  Grid Service Based on GIMP for Processing Remote Sensing Images , 2006, 2006 Eighth International Symposium on Symbolic and Numeric Algorithms for Scientific Computing.