基于稀疏表示的红外空中目标分类算法

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%.

Rotation-invariant infrared aerial target identification based on SRC

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 proposed to 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 problem. 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%.