基于小波变换的多方位角SAR图像融合方法

石泉; 李景文; 杨威; 曾虹程; 张豪杰

doi:10.13700/j.bh.1001-5965.2016.0823

基于小波变换的多方位角SAR图像融合方法

doi: 10.13700/j.bh.1001-5965.2016.0823

1.
北京航空航天大学电子信息工程学院, 北京 100083
2.
北京电子工程总体研究所, 北京 100854

基金项目:

装备预研基金 A210301HK01043

详细信息

作者简介:
石泉男, 硕士研究生。主要研究方向:SAR信号仿真与处理

杨威男, 博士, 副教授。主要研究方向:SAR系统仿真与成像处理

通讯作者:
杨威, E-mail: yangweigigi@sina.com

中图分类号: TN975
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出版历程
- 收稿日期: 2016-10-26
- 录用日期: 2017-04-02
- 网络出版日期: 2017-10-20

Multi-aspect SAR image fusion method based on wavelet transform

1.
School of Electronic and Information Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100083, China
2.
Beijing Institute of Electronic System Engineering, Beijing 100854, China

Funds:

Equipment Pre-Research Foundation A210301HK01043

More Information

Corresponding author: YANG Wei, E-mail: yangweigigi@sina.com

摘要

摘要:
针对目标散射特性空变效应以及多方位角图像信息冗余的问题，提出了一种基于小波变换和边缘检测的多方位角SAR图像融合方法。首先，将图像进行小波变换，完成低频信息和高频信息的分离，实现图像的多分辨表征及序贯图像多方位角信息的融合；然后，利用改进的Robinson算子增强图像的轮廓特征；最后，融合实验和定量评估结果证明了本文所提融合方法的有效性。
- 星载SAR /
- 多方位角图像 /
- 小波变换 /
- 边缘检测 /
- 图像融合
Abstract:
Considering the diversity of target scattering characteristics with different observation angle and information redundancy of multi-aspect images, this paper proposes a novel multi-aspect SAR image fusion method based on wavelet transform and edge detection. First, wavelet transform was performed to multi-aspect space-borne SAR image processing. The images were separated to different parts of frequency so that the multi-resolution representation and multi-aspect information of sequential images can be conveniently fused. Second, the improved Robinson edge detection algorithm was used to strengthen the energy of contour feature. Finally, fusion experiment and quantitative evaluation method were used to verify the effectiveness of this fusion imaging method.
- space-borne SAR /
- multi-aspect images /
- wavelet transform /
- edge detection /
- image fusion

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图 1 多方位角观测示意图

Figure 1. Schematic of multi-aspect observation

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图 2 待融合图像及图像差异程度

Figure 2. Image to be fused and degree of image discrepancy

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图 3 提取出的差异区域

Figure 3. Extracted zone of difference

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图 4 灰度分布直方图

Figure 4. Gray level distribution histogram

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图 5 融合流程示意图

Figure 5. Flowchart of fusion method

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图 6 图像边缘检测流程图

Figure 6. Flowchart of image edge detection

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图 7 8个方向模板

Figure 7. Direction of 8 templates

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图 8 边缘检测及融合结果

Figure 8. Edge detection and fusion result

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图 9 融合结果

Figure 9. Fusion results

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表 1 8个方向模板对应的矩阵

Table 1. Corresponding matrixes of direction of 8 templates

方向模板	0°	45°	90°	135°	180°	225°	270°	315°
对应矩阵

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表 2 锐化处理规则

Table 2. Sharpening processing rule

方向模板	锐化矩阵	锐化目标
0°		垂直方向
45°		对角方向
90°		水平方向
135°		对角方向
180°		垂直方向
225°		对角方向
270°		水平方向
315°		对角方向

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表 3 实验参数

Table 3. Experiment parameters

参数	数值
分辨率/(m×m)	1×1
距离向入射角/(°)	40.69
方位向角度范围/(°)	±2.2
波段	X波段
成像模式	聚束模式
小波函数	Daubechies
小波阶数	1
融合图像数	6

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表 4 评估结果

Table 4. Evaluation results

融合方法	信息熵	平均交叉熵	均方根交叉熵	信噪比/ dB
均值融合^[13]	6.672 3	0.126 5	0.141 9	9.264 2
加权均值融合^[13]	6.684 0	0.119 4	0.134 9	9.206 2
传统小波融合^[14]	6.604 2	0.391 0	0.428 7	8.544 0
本文方法	6.734 1	0.083 7	0.093 8	8.930 5

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