Fuzzy-ART背景抑制的单帧红外弱小目标检测

针对现有背景抑制算法未能有效地抑制背景而导致目标检测率低的问题,提出一种基于模糊自适应共振理论(Fuzzy-ART)进行背景抑制、基于行列k均值(k-means)聚类实现阈值分割的单帧红外弱小目标检测算法.首先依据红外成像原理仿真生成红外弱小目标训练样本;然后采用Fuzzy-ART神经网络建立目标模型,并以此分析各像素点的目标模糊隶属度来抑制背景杂波;最后采用基于行列k-means聚类的自适应阈值分割算法来检测真实目标.实验结果表明,该算法能有效地抑制背景杂波和突显目标,并能有效地提高信噪比检测弱小目标.

Infrared Dim Target Detection in Single Image Based on Background Suppression by Fuzzy-ART

In order to solve the problem that the current approaches cannot suppress the background clutters effectively and may result in a poor detection performance,a novel infrared dim target detection approach is presented,which is based on background suppression by fuzzy adaptive resonance theory(Fuzzy-ART) and threshold segmentation by k-means cluster of rows and columns.Firstly,infrared dim target training set is simulated according to the principle of thermal imagery.Then a Fuzzy-ART neural network is utilized to build the target models.With these models,the background clutters are suppressed according to the degree of fuzzy match between pixels and models.Lastly,the adaptive segmentation algorithm based on k-means cluster of rows and columns is utilized to detect the true targets.Experimental results show that the proposed approach is able to suppress background clutters and enhance objects effectively.It is capable of improving the signal-to-noise ratio of images and detecting targets effectively.