基于稀疏表示分类的红外空中目标分类算法
投稿时间:2019-01-09  修订日期:2019-04-17  点此下载全文
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作者单位E-mail
金璐 中国科学院上海技术物理研究所 jinlu0716@163.com 
李范鸣 中国科学院上海技术物理研究所 lfmjws@163.com 
刘士建 中国科学院上海技术物理研究所  
王霄 中国科学院上海技术物理研究所  
基金项目:国家十三五国防预研项目(Jzx2016-0404/Y72-2);上海市现场物证重点实验室基金资助项目(2017xcwzk08)
中文摘要:针对红外空中目标,提出了一种基于稀疏表示的实时分类算法。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,同时识别速率达到每秒82.6幅;此外,给测试图像50%的像素叠加噪声后,分类正确率仍大于80%。
中文关键词:红外图像  空中目标  旋转不变性  稀疏表示分类
 
Rotation-invariant Infrared Aerial Target Identification Based on SRC
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 proposed to compute 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 an 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 airfield. Experimental results show that the proposed method achieves 98.3% accuracy with 82.6FPS, and recovers the identity beyond 80% accuracy even when the test images are corrupted at 50%.
keywords:infrared image, aircraft identification, rotation invariant, sparse representation classification
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