基于特征点分布特性的激光干扰效果评估算法

Assessment Algorithm of Laser-Dazzling Effects Based on the Feature-Point Distributing Characteristic

激光主动成像系统通常用于区域监视和目标识别,但其上的光电成像探测器容易受到激光干扰,进而导致目标识别误差甚至目标丢失。因此,从目标识别特征的失效程度出发,研究激光干扰效果评估具有重要意义。提出了一种特征点相似度(FPSIM)评估算法,利用加速分割测试特征(FAST)算法提取原始图像和干扰图像的特征点,然后通过特征点匹配得到目标区域,在目标区域位置计算它们的特征点保持度和稳定度,提取原始图像特征点的位置,并在两幅图像中对应相同位置处比较它们的局部亮度和对比度失真度,再将特征点保持度、稳定度和亮度、对比度失真度相乘得到归一化的FPSIM。利用激光主动成像系统对设定目标进行照明成像实验,采集了不同干扰功率、不同背景强度和光斑位置的干扰图像。使用提出的FPSIM算法对获得的激光干扰图像进行评估,结果证明FPSIM能够客观反映图像在目标识别过程中特征点的变化情况,通过与归一化均方误差(NMSE)及结构相似度(SSIM)方法对比,FPSIM算法对不同程度的激光干扰图像都给出了合理的评估结果,其评价结果更符合主观视觉感受,并且能够指导激光主动成像识别系统的防护与应用。

Laser active imaging systems are usually used in region surveillance and target identification. However, the photoelectric imaging detector in the imaging systems is easy to be disturbed and this leads to errors of the recognition and even the missing of the target. Assessment of laser-dazzling effects in view of the invalidation of the target feature must be better understood. A new feature-point similarity （FPSIM） assessment algorithm is proposed. The feature accelerated segmentation testing （FAST） algorithm is used to extract feature points of the original image and disturbed image. The target area is obtained via feature-point matching, and the feature-point maintenance as well as stabilization is computed in the target area. The location of the feature points in the original image is obtained, and the local luminance and contrast distortion are compared in the same place of the two images. The normalized FPSIM is obtained via product of the feature-point maintenance, stabilization, luminance distortion and contrast distortion. The luminance imaging experiment is performed for the target by utilizing the laser active imaging system. In the experiment, the disturbed images of different disturbing powers, different intense backgrounds and different spot positions are obtained. The proposed FPSIM algorithm is used to evaluate the newly obtained laser-dazzling images, and the results show that the FPSIM reflects the varieties of the feature points in target recognition objectively. Compared with normalized mean square error （NMSE） and structural simila rity （SSIM）, the FPSIM gives a more reasonable evaluation result for different laser-dazzling images. The evaluation results are more suitable for the subjective visual feeling, and FPSIM can also give the guidance of the laser active imaging system defense and application.