红外与毫米波学报, 2019, 38 (5): 578, 网络出版: 2019-11-19
基于稀疏表示的红外空中目标分类算法
Rotation-invariant infrared aerial target identification based on SRC
红外图像 空中目标 旋转不变性 稀疏表示分类 infrared image aircraft identification rotation invariant sparse representation classification
摘要
针对红外空中目标,提出了一种基于稀疏表示的快速分类算法.该工作的技术难点表现在训练样本较少,算法需要具有旋转不变性、较高的抗噪性和实时性.针对这些难点,首先根据红外空中面目标的梯度信息和统计特性,计算出图像主方向,然后将主方向旋转至同一参考方向.接着基于稀疏表示原理,把分类问题转化为 1范数最小化问题,最后用快速收敛方法得到分类结果.实验结果表明该方法能够达到 98.3%的正确率,给测试图像 50%的像素叠加噪声后,分类正确率仍大于 80%.
Abstract
Aircraft identification is implemented on thermal images acquired from ground-to-air infrared cameras.SRC is proved to be an effective image classifier robust to noise,which is quite suitable for thermal image tasks.However,rotation invariance is challenging requirements in this task.To solve this issue,a method is proposedto compute the target main orientation firstly,then rotate the target to a reference direction.Secondly,an over-complete dictionary is learned from histogram of oriented gradient features of these rotated targets.Thirdly,a sparse representation model is introduced and the identification problem is converted to a l1-minimization prob. lem.Finally,different aircraft types are predicted based on an evaluation index,which is called residual error.To validate the aircraft identification method,a recorded infrared aircraft dataset is implemented in an airfield.Experimental results show that the proposed method achieves 98. 3% accuracy,and recovers the identity beyond 80% accuracy even when the test images are corrupted at 50%.
金璐, 李范鸣, 刘士建, 王霄. 基于稀疏表示的红外空中目标分类算法[J]. 红外与毫米波学报, 2019, 38(5): 578. JIN Lu, LI Fan-Ming, LIU Shi-Jian, WANG Xiao. Rotation-invariant infrared aerial target identification based on SRC[J]. Journal of Infrared and Millimeter Waves, 2019, 38(5): 578.