基于BEMD的多光谱图像弱小目标融合检测算法

Multi-spectral Dim Targets Detection Based on Bidimensional Empirical Mode Decomposition

利用二维经验模态分解(BEMD)方法在处理非线性非平稳数据方面的优势,对红外多光谱图像进行融合处理,在经典异常检测算法的基础上提出了基于二维经验模态分解的异常检测算法。由于利用了红外多光谱图像的多尺度信息,该算法可抑制背景杂波和消减高频噪声,提高目标的检测概率。红外多光谱图像的仿真实验结果表明,相比传统异常检测算法,该算法对弱小目标的检测有更好的性能。

Because the bidimensional empirical mode decomposition(BEMD) method has the advantage of dealing with non-linear and non-stationary data, the infrared multi-spectral images are fused by BEMD. A novel anomaly detection algorithm is proposed based on BEMD and classical anomaly detection algorithm to detect IR dim targets. The proposed algorithm used multi-scale information to suppress background clutter and subtract high-frequency noise. On this basis, the target detection probability is improved. Simulation experimental results show that compared with the traditional anomaly detection algorithm, the presented algorithm has a better ability to detect dim targets.