Feature selection for SAR images using the hybrid intelligent optimization algorithm

被引：0
|
作者：
Zhang Q. [1 ]
Gu Y. [1 ]
Xu Y. [2 ]
Lai X. [1 ]
机构：
[1] Fundamental Science on Communication Information Transmission and Fusion Technology Laboratory, Hangzhou Dianzi University, Hangzhou
[2] College of Life Information Science & Instrument Engineering, Hangzhou Dianzi University, Hangzhou
来源：
Yaogan Xuebao/Journal of Remote Sensing | 2016年 / 20卷 / 01期
基金：
中国国家自然科学基金;
关键词：
Feature selection; Fractal feature; Hybrid intelligent optimization algorithm; SAR image; Zernike moment;
D O I：
10.11834/jrs.20165140
中图分类号：
学科分类号：
摘要：
To improve the automatic target recognition accuracy of SAR images and real-time performance, this study proposes a feature selection algorithm based on hybrid intelligent optimization for such images. First, a fractal feature is used to enhance an SAR image. An azimuth estimation method is then developed based on the image moment after image segmentation. Subsequently, the features of Zernike moment, Gabor wavelet coefficients, and gray level co-occurrence matrix are extracted from the original and the rectified images to form feature candidates. The genetic algorithm and the binary particle swarm optimization algorithm are combined to select features for SAR images. The effectiveness of the proposed algorithm is verified with the MSTAR database. Results demonstrate that the optimal feature sets can be generalized, thereby improving the target recognition rate and reducing recognition time. © 2016, Science Press. All right reserved.
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页码：73 / 79
页数：6
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