基于边界先验双模型贝叶斯决策的红外图像海天线检测

Sea-sky Line Detection Based on Boundary Prior Double Model Bayesian Decision in Infrared Image

  • 摘要: 为了解决复杂海面干扰下海天线检测的问题,本文提出了一种基于边界先验双模型贝叶斯决策的红外图像的海天线检测方法。该方法首先将海空背景下的红外图像划分为子图像块,以子图像块的离散余弦变换的主余弦谱作为子图像块特征;然后,基于边界先验建立了海、天子图像块的贝叶斯决策模型,对海、天子图像块进行粗划分;之后,利用粗划分的子图像块集合建立细划分贝叶斯决策模型,利用重叠子图像块滑动细划分方法,获取海天线上候选点;最后,利用随机抽样一致性(RANSAC)算法得到海天线模型参数,实现海天线的检测。实验结果表明,该方法能有效检测出复杂海空背景下的海天线,对于海杂波、亮斑等干扰较多的复杂红外图像具有更优的检测效果,运算速度较快。

     

    Abstract: In order to solve the problem of underwater antenna detection with complex sea surface interference, a sea-sky line detection method based on boundary priori double model Bayesian decision in infrared image is proposed. Firstly, the infrared image in sea-sky background is divided into sub-image blocks, and the main cosine spectrum of the discrete cosine transform of sub-image blocks is used as the sub-image block feature. Then, based on the boundary priori, the Bayesian decision model of sea-sky image blocks is established to roughly divide the sea-sky image blocks. Then, the Bayesian decision of fine partition is established by using the set of rough partitioned sub-image blocks. Finally, the parameters of the sea-sky line model are obtained by using RANSAC algorithm to realize the detection of the sea-sky line. The experimental results show that the method can effectively detect the sea antenna in complex sea-sky background, and has better detection effect and faster operation speed for complex infrared images with more interference such as sea clutter and bright spot.

     

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