基于加权融合特征与Ostu分割的红外弱小目标检测算法  

Detection Algorithm for Infrared Dim Small Targets Based on Weighted Fusion Feature and Ostu Segmentation

作　　者：刘昆[1] 刘卫东[2] 

机构地区：[1]中国矿业大学徐海学院,江苏徐州221008 [2]中国矿业大学信息与电气工程学院,江苏徐州221008

出　　处：《计算机工程》2017年第7期253-260,共8页Computer Engineering

基　　金：中央高校基本科研业务费专项资金(JGC101674);江苏省自然科学基金(BK20120109)

摘　　要：为提高红外弱小目标在复杂背景干扰与低信噪比条件下的检测精度与效率,基于局部加权融合特征与分类二维Ostu分割,提出红外弱小目标检测算法。利用红外弱小目标与背景的灰度差异,基于Top-Hat算子设计红外背景过滤机制,使弱小目标从背景中凸显出来,使用图像的熵值定义局部加权融合特征,完成弱小目标的粗定位。采用分形理论计算粗定位区域内所有像素的分维值,构建像素分维像模型完成弱小目标的细定位,并通过特征分类二维Ostu分割机制实现红外弱小目标的精确检测。测试结果表明,与现有红外弱小目标检测算法相比,该算法具有更高的检测精度与更短的检测耗时。In order to improve the detection accuracy and efficiency of infrared dim small target under complex background interference and lowsignal to noise ratio,a fast detection algorithm for infrared dim small targets based on local weighted fusion feature and classification 2D Ostu segmentation is proposed. Firstly,based on Top-Hat operator,the infrared background filtering mechanism is constructed by considering the gray characteristic difference between the background and the infrared dim small target,so that the infrared dim targets is highlighted from the background area.Then,the local weighted fusion feature is defined by the entropy value of the image to finish the rough positioning of dim small targets. The fractal dimension of all pixels in coarse positioning region is calculated by using fractal theory to complete the fine positioning of the dim small target. Finally,the feature classification 2D Ostu segmentation mechanism is defined to accurately detect the dim small target. The experimental results showthat compared with the current infrared dim small target detection algorithm,the proposed algorithm has higher detection accuracy and shorter detection time.

关 键 词：红外图像 弱小目标检测 局部加权融合特征 背景过滤机制 分类二维Ostu分割 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]