自适应双极性红外舰船目标分割算法  

Adaptive Bilateral Polarity Ship Segmentation in Infrared Images

作　　者：赵菲[1] 卢焕章[1] 张志勇[1] 

机构地区：[1]国防科技大学ATR国防科技重点实验室,长沙410073

出　　处：《电子与信息学报》2012年第10期2432-2438,共7页Journal of Electronics & Information Technology

基　　金：国家863计划项目(2009AA8100701)资助课题

摘　　要：红外舰船目标分割是红外海面场景中舰船目标自动识别的关键技术之一,通过阈值方法分割舰船因具有诸多优点而被广泛应用。已有阈值算法假设已知舰船和目标的能量强度关系(一般假设舰船亮度高于背景),但实际中波红外探测器所采集的图像易受环境影响,导致成像后舰船目标呈现双极性,使得已有的阈值方法难以自适应的分割舰船目标。为解决双极性舰船目标的自适应分割问题,该文提出一种新的最大化2维熵分割算法。算法首先利用图像的多尺度局部方差-熵变化量和梯度方向方差两个指标构建2维直方图,然后使用粒子群优化算法寻找最大化2维熵的最优阈值来对图像进行粗分割,随后在粗分割的基础上进行迭代精分割获得准确的目标分割结果。实验结果表明,该文算法能够在舰船目标呈现双极性的情况下均获得较好的分割结果。Infrared ship segmentation is very important for automatic infrared ship recognition in the sea. The thresholding based algorithms are widely applied to segmentation due to the intrinsic merits. The threshold is set based on the assumption that the intensity relation between target and background is known, but the assumption is incorrect in the medium wasve infrared images. Because of the sensitivity to the environment, the target in the medium wave infrared images may be bilateral polarity, so the adaptive thresholding can not be realized. Considering the adaptive thresholding ability for segmentation of bilateral polarity ship target, a new maximum two dimensions entropy segmentation algorithm is proposed. The multi-scale local variance-entropy variety and variance of gradient direction are used to build the two dimensions histogram, the optimized threshold vector are obtained by maximizing two dimensions entropy using the particle swarm optimization algorithm. Then the fine segmentation is performed by iterative thresholding on the coarse segmentation results to get the accurate segmentation result. Experimental results indicate that the proposed algorithm can get good performance in bilateral polarity target segmentation.

关 键 词：目标检测 红外舰船分割 双极性目标 多尺度局部方差.熵变化量 梯度方向方差 

分 类 号：TN911.74[电子电信—通信与信息系统]