Sar Image Simulation with Application to Target Recognition
نویسندگان
چکیده
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. Received 15 June 2011, Accepted 5 July 2011, Scheduled 19 July 2011 * Corresponding author: Kun-Shan Chen ([email protected]). 36 Chang, Chiang, and Chen
منابع مشابه
Effects of Image Quality on SAR Target Recognition
Target recognition systems using Synthetic Aperture Radar (SAR) data require well-focused target imagery to achieve high probability of correct classification. Techniques for improving the image quality of complex SAR imagery are investigated. The application of phase gradient re-focusing of target imagery having crossrange smearing is shown to significantly improve the target recognition perfo...
متن کاملSAR Target Recognition Using Improved Fuzzy Neural Network
Target recognition in high-resolution synthetic aperture radar (SAR) images is a challenging task, because SAR images have higher ambiguity for different target, which will reduce the correct recognition rate. This paper presents an improved SAR recognition algorithm based on fuzzy neural network (FNN), which deals with the ambiguity SAR target recognition very well. This improved FNN system im...
متن کاملMicrowave Imaging Using SAR
Polarimetric Synthetic Aperture Radar (Pol.-SAR) allows us to implement the recognition and classification of radar targets. This article investigates the arrangement of scatterers by SAR data and proposes a new Look-up Table of Region (LTR). This look-up table is based on the combination of (entropy H/Anisotropy A) and (Anisotropy A/scattering mechanism α), which has not been reported up now. ...
متن کاملStatistical properties of linear correlators for image pattern classi cation with application to SAR imagery
In this paper we consider linear correlation lters for image pattern recognition, with particular application to Synthetic Aperture Radar (SAR). We investigate the statistical properties of several popular Synthetic Discriminate Function (SDF) based linear correlation lters, including SDF, MVSDF, and MACE lters. We compare these statistical properties both qualitatively and analytically for SAR...
متن کاملA new algorithm of SAR target recognition based on advance deep learning neural network
In order to improve the accuracy of synthetic aperture radar images target recognition, we have proposed a new algorithm of SAR target recognition based on advance Deep Learning neural network. The traditional radar recognition algorithm has many disadvantages, In order to improve the accuracy of synthetic aperture radar images target recognition, the author have proposed a new algorithm of SAR...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2011