基于局部重要性采样的SAR图像纹理特征提取方法

何楚; 尹莎; 许连玉; 廖紫纤

doi:10.3724/SP.J.1004.2014.00316

基于局部重要性采样的SAR图像纹理特征提取方法

doi: 10.3724/SP.J.1004.2014.00316 cstr: 32138.14.SP.J.1004.2014.00316

1.
武汉大学电子信息学院信号处理实验室武汉 430079

基金项目:

国家重点基础研究发展计划（973 计划）（2013CB733404）;国家自然科学基金（41371342，61331016）;中国博士后基金;测绘遥感信息工程国家重点实验室专项科研经费资助

详细信息

作者简介:
尹莎武汉大学电子信息学院硕士研究生. 主要研究方向为SAR图像分类.E-mail：yin00sha@gmail.com

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出版历程
- 收稿日期: 2012-06-25
- 修回日期: 2012-10-22
- 刊出日期: 2014-02-20

Feature Extraction of SAR Image Based on Local Important Sampling Binary Encoding

1.
Signal Processing Laboratory, School of Electrical Information, Wuhan University, Wuhan 430079

Funds:

Supported by National Basic Research Program of China (973 Program) (2013CB733404), National Natural Science Foundation of China (41371342, 61331016), China Postdoctoral Science Foundation Funded Project, and Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS) Special Research Funding

摘要

摘要: 合成孔径雷达（Synthetic aperture radar，SAR）图像因为相干斑现象和目标响应的空间变化呈现出一种纹理特性，局部二进编码等局部图像特征在光学纹理描述中获得较好的结果，但光学纹理特征在描述SAR图像纹理特性中因为相干成像特性往往失效. 本文在前期工作纹理特征框架的基础上，提出了一种局部重要性采样二进编码的SAR图像纹理特征（Feature extraction based on local important sampling binary，LISBF）描述方法：首先，利用样本图像对局部采样位置进行随机自适应采样，基于重要性采样（Important sample，IS）方法输出递归学习位置结果；然后，利用学习出的纹理重要采样点对进行二进特征编码；最后，通过映射和统计生成描述算子. 该特征较固定位置采样能够获取更大范围信息，同时能通过采样避免特征维数的急剧增大；通过自适应学习重要性关键点较随机采样更容易捕捉纹理固有信息；较好地适应了SAR图像极低信噪比和斑点现象的纹理. 本文将该特征用于真实图像和标准纹理库的分类研究，实验结果证明了该特征的有效性.
- 纹理特征 /
- 合成孔径雷达 /
- 局部二进编码 /
- 重要性采样
Abstract: Synthetic aperture radar (SAR) image presents properties of texture because of speckle and the spatial variability of target response. Methods of feature extraction based on local binary encoding have achieved good results and are popular with texture description in recent studies. In this paper, we make use of the framework of texture feature and propose an approach of feature extraction based on local important sampling binary encoding representation (LISBF) for SAR image classification. Firstly, use some images to sample local key points randomly and adaptively, and output the recursive learning position which is based on the important sampling method. Then, we use the learning position to form binary code. Finally, we develop the feature descriptor by mapping and statistics. This feature an provide a wider range of information than fixed location sampling and avoid the feature dimension increasing sharply by sampling. Compared with random sampling, this feature is easier to capture the texture information via adaptive learning and more fits the very low SNR and speckle of SAR image. This paper applies this feature to the classification of real SAR image and standard texture library. The effectiveness of this feature is tested and verified by the experiment results.
- Texture feature /
- synthetic aperture radar (SAR) /
- local binary code /
- important sample (IS)

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参考文献(18)

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