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Design and Realization of Remote Sensing Image Fusion Distributed Parallel Computing System Based on Globus Toolkit

Author: ZhangLiXia
Tutor: ZhangLiï¼›ZuoHaoWen
School: Lanzhou Jiaotong University
Course: Cartography and Geographic Information Systems
Keywords: Image Fusion Distributed Parallel Computing Grid computing Globus Toolkit
CLC: U237
Type: Master's thesis
Year: 2011
Downloads: 59
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With the development of remote sensing technology and sensor technology, multi-platform, multi-sensor, multi-temporal, multi-spectral, multi-angle and multi-resolution remote sensing image data is quickly coming to an alarming number. However, due to the imaging principle and application of a variety of remote sensing images for different purposes, there are certain limitations and differences of any single sensor to obtain the image data in terms of geometry, spectral and spatial resolution, the amount of information contained in the image are limited, it is difficult to meet the need of practical applications. To solve this problem, multi-sensor image data fusion technology came into being. After image fusion, the integration of a variety of image sensor imaging multiple images of the same scene into a new image, the fusion image effectively improve the credibility of the image, understandability, reduce image blur, more conducive to visual or computer processing for detection, classification, identification, understanding. Image fusion technology contains three integration levels: pixel level, feature-level and decision-making level, the paper mainly for pixel-level image fusion algorithm research. This paper first analyzes the theoretical basis of the traditional fusion algorithms, combined with experimental results and analysis of the strengths and limitations of the various algorithms, based on focus image fusion method based on IHS transform and wavelet transform, and a combination of wavelet transform and IHS transform fusion algorithm improvements; Secondly, the fused image quality evaluation method, the experimental results of the various algorithms and text quality evaluation experiments show that the improvements proposed method can effectively reduce the spectral distortion of the fusion results , at the same time improves the image spatial resolution, and better retention of the spectral information of the multi-spectral image. Massive remote sensing data, convenient, fast acquisition, image processing is becoming increasingly important, and at the same time increasing the accuracy and speed of image processing, image processing technology has brought new challenges. Remote sensing image processing is a data-intensive and computationally intensive operation, has been difficult to meet the traditional stand-alone mode image processing required for data storage, transmission, processing capacity and the emergence of distributed parallel processing system to solve this problem brought hope. The computing system with its powerful computing capabilities, unique flexibility and low costs, a strong guarantee to solve the mass data storage, transmission, analysis and management. This paper analyzes the existing problems of the traditional distributed and parallel systems, as well as the current situation of the system in the field of remote sensing data processing proposed Globus grid for distributed parallel processing system platform to build distributed parallel system for remote sensing image data processing. Grid middleware resources dynamic monitoring, shielded node heterogeneity, optimize resource selection and multi-machine collaborative computing functions. According to the architecture of the Globus Toolkit, installation configuration the Globus grid development environment, design suitable parallel mode, to achieve the task of distributing and receiving functions of the system module. Finally, the comparison of the stand-alone mode and the mode of experimental efficiency of distributed parallel system. Experimental results show that the distributed parallel systems greatly shorten the processing time and improve the processing efficiency. Thus, the grid-based distributed parallel system is a way to effectively solve the problem for mass data storage, transmission, and fast processing better choice.

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