Infrared Image Complexity Metric for Automatic Target Recognition Based on Neural Network and Traditional Approach Fusion

Infrared image complexity metric plays an important role in automatic target recognition (ATR) performance evaluation. In particular, with the development of the infrared imaging technology, there are many excellent infrared image complexity metrics for ATR. However, in the related works, there are two aspects of imperfections: (1) only the influence of individual feature is considered, ignoring the interaction among characteristics; and (2) these metrics all do not take the degradation of thermal imaging process into account. To overcome the imperfections, a novel criterion of evaluating infrared image complexity which considers the interaction among characteristics and the degradation influence is proposed. Firstly, to achieve complementary advantages among characteristics, the feature space is introduced to establish three image complexity indicators, respectively, namely feature space degradation complexity (FSDC), feature space similarity degree of global background and feature space occultation degree of local background. Each indicator is integrated by feature space to obtain complementary advantages. Secondly, to take the degradation of thermal imaging process into account, the neural network is trained to obtain the FSDC. In addition, the feature spaces are perfected by Pearson’s correlation analysis and relevant features were removed so that each indicator is more reasonable. Finally, we connect the three image complexity indicators by using an improved analytic hierarchy process. The experimental results show that the proposed algorithm is more consistent with the actual situation than traditional statistical variance and signal-to-noise ratio.

[1]  N. Kazakis,et al.  Assessment of flood hazard areas at a regional scale using an index-based approach and Analytical Hierarchy Process: Application in Rhodope-Evros region, Greece. , 2015, The Science of the total environment.

[2]  Yu Liu,et al.  Multi-focus image fusion with a deep convolutional neural network , 2017, Inf. Fusion.

[3]  Ashish Kapoor,et al.  Learning a blind measure of perceptual image quality , 2011, CVPR 2011.

[4]  Lei Yang,et al.  Detection of small targets with adaptive binarization threshold in infrared video sequences , 2006 .

[5]  Jie Yan,et al.  Complexity Metric of Infrared Image for Automatic Target Recognition , 2018, 2018 3rd International Conference on Computational Intelligence and Applications (ICCIA).

[6]  A. Beghdadi,et al.  Image quality assessment using a neural network approach , 2004, Proceedings of the Fourth IEEE International Symposium on Signal Processing and Information Technology, 2004..

[7]  Kishor M. Bhurchandi,et al.  No-reference image quality assessment algorithms: A survey , 2015 .

[8]  Jin Duan,et al.  Research on the optimal selection method of image complexity assessment model index parameter , 2015, Applied Optics and Photonics China.

[9]  Gianluigi Ciocca,et al.  Predicting Complexity Perception of Real World Images , 2016, PloS one.

[10]  Randeep Singh.  Basic of Artificial Neural Network , 2018 .

[11]  Sushil Kumar,et al.  Analytic hierarchy process: An overview of applications , 2006, Eur. J. Oper. Res..

[12]  David L. Wilson.  Image-based contrast-to-clutter modeling of detection , 2001 .

[13]  Ralph Stephen Haller.  Complexity of real images evaluated by densitometric analysis and by psychophysical scaling , 1970 .

[14]  Nikolay N. Ponomarenko,et al.  Image database TID2013: Peculiarities, results and perspectives , 2015, Signal Process. Image Commun..

[15]  Bir Bhanu,et al.  Automatic Target Recognition: State of the Art Survey , 1986, IEEE Transactions on Aerospace and Electronic Systems.

[16]  Wei Liu,et al.  A novel infrared and visible image fusion algorithm based on shift-invariant dual-tree complex shearlet transform and sparse representation , 2017, Neurocomputing.

[17]  A. Rogalski,et al.  Semiconductor detectors and focal plane arrays for far-infrared imaging , 2013 .

[18]  Y Liu.  Review of Infrared Image Complexity Evaluation Method , 2014 .

[19]  Gianluigi Ciocca,et al.  Does Color Influence Image Complexity Perception? , 2015, CCIW.

[20]  Philip Sedgwick,et al.  Pearson’s correlation coefficient , 2012, BMJ : British Medical Journal.

[21]  A A Brewis,et al.  Hydatidiform mole pregnancy in Micronesian women. , 1996, The New Zealand medical journal.

[22]  Xiubao Sui,et al.  A novel non-uniformity evaluation metric of infrared imaging system , 2013 .

[23]  Alan C. Bovik,et al.  Making a “Completely Blind” Image Quality Analyzer , 2013, IEEE Signal Processing Letters.

[24]  Gordon Erlebacher,et al.  Hybrid No-Reference Natural Image Quality Assessment of Noisy, Blurry, JPEG2000, and JPEG Images , 2011, IEEE Transactions on Image Processing.

[25]  Raimondo Schettini,et al.  How to assess image quality within a workflow chain: an overview , 2014, International Journal on Digital Libraries.

[26]  Caroline Jay,et al.  Analysing the visual complexity of web pages using document structure , 2013, Behav. Inf. Technol..

[27]  Xia Mao,et al.  Criterion to Evaluate the Quality of Infrared Small Target Images , 2009 .

[28]  Bin Kong,et al.  A study of object detection based on fuzzy support vector machine and template matching , 2004, Fifth World Congress on Intelligent Control and Automation (IEEE Cat. No.04EX788).