针对合成孔径声纳(SAS)图像不均衡问题，提出一种基于时变曲线模型的SAS图像自动均衡方法。以声传播模型、水底后向散射模型和SAS成像模型为基础，推导时变曲线（TVC）的表达式；结合SAS图像的统计特征，推导TVC观测量的计算方法；用非线性最小二乘拟合方法完成TVC估计；基于TVC进行了SAS图像的自动均衡。用湖试和海试数据对该方法进行了验证，结果表明推导的TVC表达式与试验数据具有较好的吻合度，提出的自动均衡方法可有效地消除SAS图像的不均衡问题。
Abstract
A time-variant curve (TVC) model-based automatic equalization method is proposed for the intensity variation problem of synthetic aperture sonar (SAS) images. A theoretical expression of TVC is deduced based on sound transmission model, underwater backscattering strength model and SAS image model. A method to calculate the TVC observations is established based on the statistical model of SAS images. An estimation method based on non-linear least square fitting model (NL-LSFM) is used to acquire the optimized parameter of TVC. At last, the SAS images are automatically equalized and enhanced based on the optimized TVC. The method proposed has been validated by lake and sea trials. The test results show that the result calculated by the TVC expression is consistent with the experimental data, and the automatic equalization method can remove the intensity variation of SAS images properly.
关键词
信息处理技术 /
合成孔径声纳 /
图像均衡 /
时变曲线 /
威布尔分布
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Key words
information processing /
synthetic aperture sonar /
image equalization /
time-variant curve /
Weibull distribution
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基金
国家自然科学基金项目(11204343)；哈尔滨工程大学水下机器人技术重点实验室基金项目（9140C27020112022601）
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脚注
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