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Study and Implementation on Rapidly Preprocessing Method of Remote Sensing Image

Author: CaoLingLing
Tutor: ZhangYongMei
School: University of North
Course: Computer Software and Theory
Keywords: Remote sensing image Geometric correction Parallel algorithms Image matching The ranks of separation
CLC: TP751
Type: Master's thesis
Year: 2011
Downloads: 59
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Abstract


With the development of human technology , humanity has entered a new digital era , the rise of remote sensing technology . Remote sensing technology has been widely applied to all aspects of the state and society , while the massive extent and complexity of the data it brings unprecedented data reliability requirements of these applications have become more sophisticated . Image world and an important source of human knowledge , which contains a lot of important information , remote sensing image processing is an important branch of digital image processing . Subject to many external factors interfere with remote sensing image in the imaging process , resulting in geometric distortion and affect the quality of the image , its geometric precision correction of remote sensing image fast processing is also a pressing problem , parallel technology is an effective way to solve the fast image processing . Firstly, the remote sensing image is divided into a number of blocks , the use of process simulation of parallel algorithms for geometric precision correction , and image processing block at the edge of each process redundancy certain lines , in order to avoid inter-process communication , image pixel weight sampling using ranks of the separation method, and tested to verify the method to accelerate the processing speed of the image can be achieved while ensuring the geometric correction accuracy . Feature is the equivalent of a pixel neighborhood a result of the nature of performance , can keep the translation, rotation invariance . The feature-based matching with high reliability and stability . SIFT feature point detection method to extract the characteristics of remote sensing image , the traditional 16 × 16 Gaussian weighted range of feature point descriptor form 128 dimensional SIFT eigenvectors , increased computational feature matching ring article uses the eigenvectors reducing the number of dimensions to 36 dimensions , and then use the Euclidean distance characteristics match Finally the RANSAC eliminate false matching transform relations between the two the pending match the image with the least squares fit to the same ranks as $ resampling separation the method, tested to verify that the method can effectively improve the efficiency of the registration .

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CLC: > Industrial Technology > Automation technology,computer technology > Remote sensing technology > Interpretation, identification and processing of remote sensing images > Image processing methods
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