基于多波段多极化仿真图像的SAR目标识别研究

doi:10.16182/j.issn1004731x.joss.201710032

系统仿真学报 ›› 2017, Vol. 29 ›› Issue (10): 2482-2488.doi: 10.16182/j.issn1004731x.joss.201710032

基于多波段多极化仿真图像的SAR目标识别研究

谷雨¹, 张琴¹, 徐英²

1.杭州电子科技大学通信信息传输与融合技术国防重点学科实验室,浙江杭州 310018;
2.杭州电子科技大学生命信息与仪器工程学院,浙江杭州 310018

收稿日期:2015-10-12 发布日期:2020-06-04
作者简介:谷雨(1982-),男,吉林双阳,博士,副教授,研究方向为SAR目标识别、多源信息融合。
基金资助:
国家自然科学基金(61174024, 61372024),浙江省自然科学基金(LQ13F050010)

SAR Object Recognition Based on Multi-band and Multi-polarization Simulation Image

Gu Yu¹, Zhang Qin¹, Xu Ying²

1. Fundamental Science on Communication Information Transmission and Fusion Technology Laboratory,Hangzhou Dianzi University, Hangzhou 310018, China;
2. College of Life Information Science & Instrument Engineering, Hangzhou Dianzi University, Hangzhou 310018, China

Received:2015-10-12 Published:2020-06-04

摘要/Abstract

摘要： 利用Creator建立目标模型,Vega的TMM工具进行纹理材质映射,基于视景仿真技术建立了多波段多极化SAR图像数据库。设计了融合遗传算法和二值粒子群的混合智能优化算法,对SAR图像的波段极化组合方式进行优化;基于未矫正和矫正后的图像分别提取Zernike矩、Gabor小波系数等构成候选特征序列,进行了多波段多极化SAR图像特征选择实验。实验结果表明,采用仿真技术建立SAR图像数据库是进行多波段多极化SAR图像识别的一种有效手段;采用优化后的特征集合能够提高多波段多极化SAR图像的识别率。

关键词: SAR, 多波段多极化, 计算机仿真, 特征选择

Abstract: The object model was built based on Creator, and object texture-material mapping was performed by Vega TMM tool. The multi-band and multi-polarization SAR image database was built by visual simulation technology. A hybrid intelligent optimization algorithm was designed to optimize combination of band and polarization by genetic algorithm and binary particle optimization. Zernike moment features, Gabor wavelet coefficients, etc were extracted from original image and rectified image to make up of feature candidates, and the feature selection experiments were carried out by using multi-band and multi-polarization SAR images. Simulation results demonstrate that, building SAR image database through simulation technology is an effective method to perform research on multi-band and multi-polarization SAR image recognition; target recognition rate can be improved for multi-band and multi-polarization SAR images using the optimized feature set.

Key words: SAR, multi-band and multi-polarization, computer simulation, feature selection

中图分类号:

TP753

谷雨, 张琴, 徐英. 基于多波段多极化仿真图像的SAR目标识别研究[J]. 系统仿真学报, 2017, 29(10): 2482-2488.

Gu Yu, Zhang Qin, Xu Ying. SAR Object Recognition Based on Multi-band and Multi-polarization Simulation Image[J]. Journal of System Simulation, 2017, 29(10): 2482-2488.

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基于多波段多极化仿真图像的SAR目标识别研究

SAR Object Recognition Based on Multi-band and Multi-polarization Simulation Image

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