摘要
针对天空背景红外图像中弱小目标检测的难题,分析了红外目标检测的模型,提出了基于稀疏环决策的目标检测算法。利用数学形态学滤波目标增强方法对图像进行背景抑制,而后采用恒虚警检测方法对滤波后图像进行自适应分割,从而获得候选目标点,然后计算各个候选目标点的局部自相似性描述子,对自相似性描述子归一化、分块之后得到稀疏环表示,利用相应的判断准则可以判别目标点与虚警点。实验结果表明,该算法应用于复杂云层背景弱小红外目标图像能够得到较理想的结果,与移动管道滤波方法相比,能有效区别目标点与固定云层杂波干扰,并且虚警率低,易于实现。
Aiming at the problem of dim target detection in infrared images with sky background,the detection model of infrared targets is analyzed and a detection algorithm was put forward based on the sparse ring decision. The morphological filtering target enhancement method was used for background suppression,then the Constant False Alarm Rate（ CFAR） detection method was adopted for image adaptive segmentation to get candidate target points. The Local Self-Similarity（ LSS） descriptors of candidate target points were calculated out,and sparse ring was obtained by normalizing the LSS descriptors and partitioning. By means of appropriate criterion,the target point and false alarm points can be distinguished. Experiments show that： 1） The algorithm can get ideal results when applied to infrared images with dim and small target against clutter cloud background; and2） Comparing with moving pipeline filter algorithm,it has lower false alarm rate and is easier for implementation.
出处
《电光与控制》
北大核心
2015年第4期32-35,53,共5页
Electronics Optics & Control
基金
国家自然科学基金(61273075)
关键词
目标检测
红外目标
弱小目标
稀疏环
形态学滤波
恒虚警
target detection
infrared target
dim target
sparse ring
morphological filtering
CFAR
