基于包围盒约束光谱聚类的红外目标识别算法

在红外成像过程中，目标边缘模糊化是影响红外目标识别效果的关键因素，也是红外目标识别算法的研究重点，故在光谱图像中合理补偿目标几何特征信息成为研究热点之一。结合包含目标几何特征信息的包围盒作为约束条件，对红外光谱图像进行分层限定滤波，降低原有图像数据中目标几何外形数据的丢失，提高目标可识别性。设计了在包围盒约束条件下的光谱聚类算法，设置参数η表征待测军用车辆目标的几何信息，设置参数m表征待测军用车辆目标的光谱特征信息。实验采用TEL-1000-MW型红外成像光谱仪获取多光谱图像，通过改变m和η值调整光谱特征值个数与包围盒范围，从而获得不同的目标识别图像。并与传统方法对同一幅红外目标图像的识别效果相比较，结果发现采用包围盒约束的待测目标图像几何边界信息保留效果明显优于传统方法，当m=10、η=0.7时，红外图像的目标识别效果最好，同时算法收敛速度也最优。由此可见，该算法在提高红外目标识别能力、避免误判伪目标和漏检目标方面具有很高的实用价值。

Infrared target recognition algorithm based on bounding box constrained spectral clustering

In the process of infrared imaging, target edge blurring is a key factor that affects the effect of infrared target recognition, and it is also the focus of infrared target recognition algorithms. Therefore, reasonable compensation of target geometric feature information in spectral images has become one of the research hotspots.The bounding box containing the geometric feature information of the target was used as a constraint condition, and the infrared spectrum image was hierarchically limited and filtered to reduce the loss of the target geometric shape data in the original image data and improve the recognizability of the target. A spectral clustering algorithm under bounding box constraints was designed. The parameter η was set to characterize the geometric information of the military vehicle target under test, and the parameter m was set to characterize the spectral feature information of the military vehicle target under test. In the experiment, a TEL-1000-MW infrared imaging spectrometer was used to obtain multi-spectral images. By changing the m and η values, the number of spectral feature values and the bounding box range were adjusted to obtain different target recognition images. Compared with the traditional method for the recognition effect of the same infrared target image, it was found that the geometric boundary information retention effect of the target image under test using the bounding box constraint was significantly better than that of the traditional method. When m=10, η=0.7, the infrared image target recognition effect was the best, and the algorithm convergence speed was also the best. It can be seen that the algorithm has high practical value in improving the ability of infrared target recognition and avoiding false targets and missed targets.