基于结构张量筛选和局部对比度分析的空中红外小目标检测算法

Aerial Infrared Small Target Detection Algorithm Based on Structure Tensor Screening and Local Contrast Analysis

针对复杂云层背景下红外小目标检测的虚警现象和实时性要求,提出一种基于结构张量筛选和局部对比度分析的新算法。结合目标区域结构张量最大特征值大于其他背景区域结构张量最大特征值的特点,滤除大部分非目标区域,保留少量可疑区域,再对可疑区域进行局部对比度计算,能够增强目标、抑制残留背景,并有效减少计算量。算法步骤如下:首先,在滑动窗口捕获的局部图像区域内构建结构张量矩阵,将最大特征值大于特定阈值的区域标记为可疑区域;然后,对可疑区域进行比差联合型局部对比度计算,生成显著度图;最后,利用自适应阈值分割实现小目标的分离。实验结果表明:该算法在复杂云层背景下具有更高的检测率、更低的虚警率以及更少的运行时间。

Considering the false alarm and real-time requirements of infrared small-target detection under a complex cloud background,a novel algorithm is proposed based on structure tensor screening and local contrast analysis.Combined with the feature that the maximum eigenvalue of the structure tensor of the target area is larger than that of other background areas,the proposed algorithm can filter out most nontarget areas and retain a few suspicious areas.Local contrast calculation performed on suspicious areas can enhance the target,suppress the residual background,and effectively reduce computation.The algorithm steps are as follows:first,we constructed the structure tensor matrix within the local image area captured by the sliding window,and where the maximum eigenvalue is larger than the threshold is marked as a suspicious area.Then,we calculated the ratio-difference joint local contrast.Finally,we adopted an adaptive threshold segmentation on the saliency map to extract the real target.Experimental results showed that the proposed algorithm can achieve a higher detection rate,lower false alarm rate,and shorter running time under a complex cloud background.