A sparse representation-based method for infrared dim target detection under sea–sky background

Abstract Automatic detection for infrared (IR) dim targets under complex sea–sky background is a challenging task. To explore an effective solution to the problem, this paper develops a sparse representation-based method by learning a sea–sky background dictionary. This framework is mainly composed of three modules: background dictionary learning, preliminary target localization, and accurate target identification. In the first module, a sea–sky background dictionary is learned from a large number of training samples, which has a good ability to model the cluttered sea–sky background. In the second module, given a test image, it is first divided into a set of patches; then, for each image patch, its sparse representation coefficients are computed over the learned dictionary. By analyzing the sparse reconstruction errors for the image patches, the target candidate areas can be predicted. In the third module, an infrared dim target recognition scheme is applied to those areas to recognize the true dim IR targets. Based on a set of comprehensive experiments, our algorithm has demonstrated better performance than several other infrared dim target detection methods.

[1]  Junzhou Huang,et al.  Robust Visual Tracking Using Local Sparse Appearance Model and K-Selection , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Zhang Peng,et al.  The design of Top-Hat morphological filter and application to infrared target detection , 2006 .

[3]  Jie Yang,et al.  Infrared small target detection using sparse representation , 2011 .

[4]  Lei Guo,et al.  Effective and Efficient Midlevel Visual Elements-Oriented Land-Use Classification Using VHR Remote Sensing Images , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[5]  Xin Wang,et al.  Infrared dim target detection based on visual attention , 2012 .

[6]  M. Elad,et al.  $rm K$-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation , 2006, IEEE Transactions on Signal Processing.

[7]  Lei Guo,et al.  An Object-Oriented Visual Saliency Detection Framework Based on Sparse Coding Representations , 2013, IEEE Transactions on Circuits and Systems for Video Technology.

[8]  Xinsheng Huang,et al.  Infrared dim and small target detecting and tracking method inspired by Human Visual System , 2014 .

[9]  Fei Zhang,et al.  Edge directional 2D LMS filter for infrared small target detection , 2012 .

[10]  Yujie He,et al.  Small infrared target detection based on low-rank and sparse representation , 2015 .

[11]  Michael Elad,et al.  Sparse Representation for Color Image Restoration , 2008, IEEE Transactions on Image Processing.

[12]  Liangpei Zhang,et al.  Sparse Transfer Manifold Embedding for Hyperspectral Target Detection , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[13]  Junwei Han,et al.  Efficient, simultaneous detection of multi-class geospatial targets based on visual saliency modeling and discriminative learning of sparse coding , 2014 .

[14]  Yihua Tan,et al.  Biologically inspired multilevel approach for multiple moving targets detection from airborne forward-looking infrared sequences. , 2014, Journal of the Optical Society of America. A, Optics, image science, and vision.

[15]  Jinwen Tian,et al.  Novel method on dual-band infrared image fusion for dim small target detection , 2007 .

[16]  Tianxu Zhang,et al.  Clutter-adaptive infrared small target detection in infrared maritime scenarios , 2011 .

[17]  Stéphane Mallat,et al.  Matching pursuits with time-frequency dictionaries , 1993, IEEE Trans. Signal Process..

[18]  Joel A. Tropp,et al.  Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit , 2007, IEEE Transactions on Information Theory.

[19]  Guillermo Sapiro,et al.  Non-local sparse models for image restoration , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[20]  Larry S. Davis,et al.  Label Consistent K-SVD: Learning a Discriminative Dictionary for Recognition , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[21]  Qi Li,et al.  Sparse-representation-based automatic target detection in infrared imagery , 2013 .

[22]  Jean Ponce,et al.  Task-Driven Dictionary Learning , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[23]  Michael Elad,et al.  Image Denoising Via Sparse and Redundant Representations Over Learned Dictionaries , 2006, IEEE Transactions on Image Processing.

[24]  Zhang Jianqi,et al.  Homogeneous background prediction algorithm for detection of point target , 2011 .

[25]  Michael A. Saunders,et al.  Atomic Decomposition by Basis Pursuit , 1998, SIAM J. Sci. Comput..

[26]  Lei Liu,et al.  Infrared dim target detection based on fractal dimension and third-order characterization , 2009 .

[27]  Lizhong Xu,et al.  Spatiotemporal saliency model for small moving object detection in infrared videos , 2015 .

[28]  Chen Wang,et al.  A Kernel-Based Nonparametric Regression Method for Clutter Removal in Infrared Small-Target Detection Applications , 2010, IEEE Geoscience and Remote Sensing Letters.

[29]  Junwei Han,et al.  Multi-class geospatial object detection and geographic image classification based on collection of part detectors , 2014 .

[30]  Tanaya Guha,et al.  Learning Sparse Representations for Human Action Recognition , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.