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    题名: SAR IMAGE SIMULATION WITH APPLICATION TO TARGET RECOGNITION
    作者: Chang,YL;Chiang,CY;Chen,KS
    贡献者: 太空及遙測研究中心
    关键词: LEARNING NEURAL-NETWORK;LOCAL STATISTICS;CLASSIFICATION;CLUTTER;RCS
    日期: 2011
    上传时间: 2012-03-27 17:49:45 (UTC+8)
    出版者: 國立中央大學
    摘要: This paper presents a novel synthetic aperture radar (SAR) image simulation approach to target recognition, which consists of two frameworks, referred to as the satellite SAR images simulation and the target recognition and identification. The images simulation makes use of the sensor and target geo-location relative to the Earth, movement of SAR sensor, SAR system parameters, radiometric and geometric characteristics of the target, and target radar cross section (RCS), orbital parameters estimation, SAR echo signal generation and image focusing to build SAR image database. A hybrid algorithm that combines the physical optics, physical diffraction theory, and shooting and bouncing rays was used to compute the RCS of complex radar targets. Such database is vital for aided target recognition and identification system Followed by reformulating the projection kernel in an optimization equation form, the target's reflectivity field can be accurately estimated. Accordingly, the target's features can be effectively enhanced and extracted, and the dominant scattering centers are well separated. Experimental results demonstrate that the simulated database developed in this paper is well suited for target recognition. Performance is extensively tested and evaluated from real images by Radarsat-2 and TerraSAR-X. Effectiveness and efficiency of the proposed method are further confirmed.
    關聯: PROGRESS IN ELECTROMAGNETICS RESEARCH-PIER
    显示于类别:[太空及遙測研究中心] 期刊論文

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