Rotation-invariant infrared aerial target identification based on SRC
Received:January 09, 2019  Revised:July 08, 2019  download
Citation:
Hits: 11
Download times: 2
Author NameAffiliationE-mail
JIN Lu Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China
University of Chinese Academy of Sciences, Beijing 100049, China
CAS Key Laboratory of Infrared System Detection and Imaging Technology,Shanghai Institute of Technical Physics, Shanghai 200083, China 
jinlu0716@163.com 
LI Fan-Ming Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China
CAS Key Laboratory of Infrared System Detection and Imaging Technology,Shanghai Institute of Technical Physics, Shanghai 200083, China 
lfmjws@163.com 
LIU Shi-Jian Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China
CAS Key Laboratory of Infrared System Detection and Imaging Technology,Shanghai Institute of Technical Physics, Shanghai 200083, China 
 
WANG Xiao Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China
University of Chinese Academy of Sciences, Beijing 100049, China
CAS Key Laboratory of Infrared System Detection and Imaging Technology,Shanghai Institute of Technical Physics, Shanghai 200083, China 
 
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 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%.
keywords:infrared image  aircraft identification  rotation invariant  sparse representation classification
View Full Text  HTML  View/Add Comment  Download reader

Copyright:《Journal of Infrared And Millimeter Waves》