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Research on the Pre-processing Methods for Vehicle Target Classification in SAR Images

Author: HuangJiaXin
Tutor: LuJun
School: National University of Defense Science and Technology
Course: Information and Communication Engineering
Keywords: Synthetic Aperture Radar Automatic Target Recognition Image Enhancement Azimuth estimation Regularization Target classification
CLC: TN957.52
Type: Master's thesis
Year: 2011
Downloads: 29
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Abstract


In the past two decades, synthetic aperture radar (SAR) technology has made leaps and bounds. Due to the the SAR initiative to launch the electromagnetic waves can penetrate clouds, regardless of the weather and time constraints, we can all-time, all-weather to provide the required information. At the same time, with the progress of the SAR image acquisition capabilities, SAR images Automatic Target Recognition (ATR) is gradually become an important aspect of the SAR image interpretation. However, the SAR side imaging and coherent imaging characteristics determine the geometric distortion and may contain a large number of multiplicative speckle noise, difficult to direct application of traditional image processing techniques that affect the accuracy of the automatic target recognition of SAR images and soundness. Therefore, before carrying automatic target recognition, generally target slice some preliminary image preprocessing. Pretreatment of the SAR image can be largely suppressing the noise of the target slice, to enhance the resolution of the image, some of the characteristics of the target slice, and can be extracted by preprocessing, such as the contour characteristic of the target, azimuth information, etc., for subsequent The identification algorithm has laid a good foundation to improve the accuracy and efficiency of the classification. Therefore, the SAR image pre-processing is an important part of the SAR image processing. SAR target classification method for simulation-based image template, due to the differences between the simulated images and measured image preprocessing links are particularly critical. This article focuses on the azimuth estimates, target enhancement pretreatment technology research, in order to improve simulation image template-based SAR target classification performance. SAR image target azimuth estimation based envelope box azimuth estimation method as well as a combination of both azimuth estimation method based on the led border azimuth estimation method, and put forward based on the dominant boundary Radon transform SAR target azimuth estimation, the method use to target leading boundary length divided image characteristics, to solve the problem of the leading boundary of the traditional use of the estimated target vertical and horizontal orientation of the fuzzy. MSTAR measured data experimental results show good accuracy and robustness of the proposed algorithm. In SAR image target enhancement, analysis and comparison of the differences between the measured image and simulation images, and thus propose to apply regular means to target image enhancement based MSTAR target data based lk ??norm is algorithm experimental verification, the results show that this method can effectively reduce the differences between the measured image and simulation images to improve the signal-to-noise ratio of the image, and this laid a good foundation for the SAR target classification method based on simulation image below. Finally, in order to verify the impact of the the preprocessing means of SAR image target classification effect the level of the pixel level and characteristics build two object classification method based on simulation template, the simulation image template-based data and MSTAR measured data were compared with experimental, The results showed that the pretreatment means significantly improved target classification performance, and built SAR target classification can effectively reduce the template inventory reserves, saving the measured data acquisition costs, has a practical application.

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CLC: > Industrial Technology > Radio electronics, telecommunications technology > Radar > Radar equipment,radar > Radar receiving equipment > Data,image processing and admission
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