Infrared dim target detection method based on the fuzzy accurate updating symmetric adaptive resonance theory

Abstract Motivated by the human visual system, we propose an infrared dim target detection method that is based on a fuzzy accurate updating symmetric adaptive resonance theory network. From the bottom-up perspective, the regions of interest (ROIs) are extracted using a difference of Gaussians at multiple scales and our designed ROI model in a saliency map. From the top-down perspective, five feature categories are extracted using the ROI model, which are used to train the proposed Fuzzy AUSART network. The well-trained network realizes the true identification of all ROI candidates. The results of the receiver operating characteristic (ROC) curves verify that the proposed method can better adapt to different circumstances and targets in our experiment. The average detection accuracy of the Fuzzy AUSART is improved by 15.4%, and the average F1 index of the proposed method is higher than six typical comparison methods by more than a factor of 2.48.

[1]  Stephen Grossberg,et al.  Fuzzy ART: Fast stable learning and categorization of analog patterns by an adaptive resonance system , 1991, Neural Networks.

[2]  Yihua Tan,et al.  Unsupervised Multilayer Feature Learning for Satellite Image Scene Classification , 2016, IEEE Geoscience and Remote Sensing Letters.

[3]  Zengping Chen,et al.  A real-time target detection algorithm for Infrared Search and track system based on ROI extraction , 2012 .

[4]  Xin Zhou,et al.  Entropy-based window selection for detecting dim and small infrared targets , 2017, Pattern Recognit..

[5]  Simone Frintrop,et al.  Computational Visual Attention , 2011, Computer Analysis of Human Behavior.

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

[7]  Maoguo Gong,et al.  Remote Sensing Image Registration With Modified SIFT and Enhanced Feature Matching , 2017, IEEE Geoscience and Remote Sensing Letters.

[8]  Yongjun Zhang,et al.  A novel spatio-temporal saliency approach for robust dim moving target detection from airborne infrared image sequences , 2016, Inf. Sci..

[9]  Jianping Gou,et al.  A unified Bayesian mixture model framework via spatial information for grayscale image segmentation , 2016, J. Vis. Commun. Image Represent..

[10]  Yi Lu Murphey,et al.  A Supervised Fuzzy Adaptive Resonance Theory with Distributed Weight Update , 2010, ISNN.

[11]  Yantao Wei,et al.  Multiscale patch-based contrast measure for small infrared target detection , 2016, Pattern Recognit..

[12]  Yang Ting-wu,et al.  Background suppression for dim small target with Gabor kernel non-local means , 2009 .

[13]  Yuan Yan Tang,et al.  A Local Contrast Method for Small Infrared Target Detection , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[14]  Kristine E. Matthews,et al.  A Bayes decision test for detecting uncovered-background and moving pixels in image sequences , 1998, IEEE Trans. Image Process..

[15]  Jun-Bao Li,et al.  An infrared small target detection framework based on local contrast method , 2016 .

[16]  Samuel Kaski,et al.  Winner-take-all networks for physiological models of competitive learning , 1994, Neural Networks.

[17]  Jing Chen,et al.  Sparse Representation for Infrared Dim Target Detection via a Discriminative Over-Complete Dictionary Learned Online , 2014, Sensors.

[18]  Xiaochun Cao,et al.  Cluster-Based Co-Saliency Detection , 2013, IEEE Transactions on Image Processing.

[19]  Lucia Maddalena,et al.  A Self-Organizing Approach to Background Subtraction for Visual Surveillance Applications , 2008, IEEE Transactions on Image Processing.

[20]  Jun-Bao Li,et al.  An infrared small target detection algorithm based on high-speed local contrast method , 2016 .

[21]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

[22]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[23]  Li Wan.  A Survey of Visual Attention Based Methods for Object Tracking , 2014 .

[24]  Jinping Sun,et al.  PHD and CPHD Filtering With Unknown Detection Probability , 2018, IEEE Transactions on Signal Processing.

[25]  Ruigang Yang,et al.  A new infrared small and dim target detection algorithm based on multi-directional composite window , 2015 .

[26]  Chul Lee,et al.  Contrast enhancement of noisy low-light images based on structure-texture-noise decomposition , 2017, J. Vis. Commun. Image Represent..