CAJ | 학술논문

对于辐射源边缘呈非线性变化的复杂图像，用背景预测的方法对红外弱小目标进行检测时，传统的固定权值(CW)方法效果比较差。在固定权值算法的基础上，引入了k-最近邻(k- NN)分类判别决策，提出了一种基于k-最近邻方法的红外点目标检测算法。先确定了预测窗口的大小，再通过计算方差和偏倚优化了最近邻参数k。实验结果表明，该算法在抑制背景、增强目标方面都有较好的优越性。它使预测的背景图像较好地避开离散信息，进而逼近背景的真实情况，为进一步滤除背景打下良好的基础。
대우복사원변연정비선성변화적복잡도상，용배경예측적방법대홍외약소목표진행검측시，전통적고정권치(CW)방법효과비교차。재고정권치산법적기출상，인입료k-최근린(k- NN)분류판별결책，제출료일충기우k-최근린방법적홍외점목표검측산법。선학정료예측창구적대소，재통과계산방차화편의우화료최근린삼수k。실험결과표명，해산법재억제배경、증강목표방면도유교호적우월성。타사예측적배경도상교호지피개리산신식，진이핍근배경적진실정황，위진일보려제배경타하량호적기출。
As one of the background estimation algorithms for Infrared (IR) point target detection, the performance of constant weight (CW) method is poor to the complex nonlinear background. Therefore, a k- Nearest Neighbor (k- NN) discriminant decision is been lead to the CW. Furthermore, a k- NN algorithm for IR target detection was proposed. In order to filter out the complex nonlinear background, we the size of predicted window was confirmed first, and then the parameter by calculating the variance and bias of original and predicted image was optimized. It is shown by IR images detection experiments that the k-NN method improves the performance of detection in suppression of background and enhancement of target. It can predicted the background approximately and avoid discrete information better.