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一种多光谱红外舰船目标融合检测方法
投稿时间:2018-07-23  修订日期:2018-08-06  点此下载全文
引用本文:詹维,仇荣超,刘军,马新星.一种多光谱红外舰船目标融合检测方法[J].红外,2018,39(9):41~48
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作者单位E-mail
詹维 海军航空大学 weleschan@126.com 
仇荣超 海军航空大学  
刘军 海军航空大学  
马新星 海军航空大学  
中文摘要:针对复杂岸岛背景下的红外舰船目标检测问题,提出了一种多光谱融合红外舰船目标检测方法。首先根据不同谱段信息相互间的关系进行基于非下采样轮廓波变换(Nonsubsampled Contourlet Transform, NSCT)域的多级多光谱图像融合,然后利用LSD线段检测和聚类对融合后的图像进行岸岛线检测。采用选择性搜索算法生成初始目标候选区域,然后结合岸岛线空间位置以及舰船目标的几何特征和灰度特征约束剔除部分虚假目标区域,最后提取候选区域的方向梯度直方图(Histogram of Oriented Gradient, HOG)特征算子。利用线性支持向量机(Support Vector Machine, SVM)分类器进行分类识别,以检测出真实舰船目标。实验结果表明,与单谱段红外舰船目标检测方法相比,本文方法在检测精度上有较大提升。
中文关键词:多光谱  舰船图像  选择性搜索  目标检测  SVM
 
A Multispectral Infrared Ship Target Fusion Detection Method
Abstract:To implement the detection of infrared ship targets in the complex shore island background, an infrared ship target detection and recognition method based on multi-spectral fusion is proposed. Firstly, Nonsubsampled Contourlet Transform (NSCT) multi-level multi-spectral image fusion is performed according to the relationship between different spectral segments. Then, in combination with LSD line segment detection and clustering, shoreline is detected in the fusion image. A selective search algorithm is used to generate the initial target candidate region. The spatial location of the shoreline, the ship target geometric feature and the gray feature constraint are used to eliminate some false target regions. Finally, the feature descriptors in the Histogram of Oriented Gradient (HOG) are extracted in the candidate region. The linear Support Vector Machine (SVM) classifier is used to classify and identify the real ship targets. The experimental results show that compared with the single spectral band infrared ship target detection method, the proposed method has a better detection accuracy.
keywords:multispectral  ship image  selective search  target detection  SVM
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