SAR image target detection in complex environments based on improved visual attention algorithm

A novel target detection algorithm for synthetic aperture radar (SAR) images based on an improved visual attention method is proposed in this paper. With the development of SAR technology, target detection algorithms are confronted with many difficulties such as a complicated environment and scarcity of target information. Visual attention of the human visual system can make humans easily focus on key points in a complex picture, and the visual attention algorithm has been used in many fields. However, existing algorithms based on visual attention models cannot obtain satisfactory results for SAR image target detection under complex environmental conditions. After analysing the existing visual attention models, we combine the pyramid model of visual attention with singular value decomposition to simulate the human retina, which can make the visual attention model more suitable to the characteristics of SAR images. We introduce variance weighted information entropy into the model to optimize the detection results. The results obtained by the existing visual attention algorithm for target detection in SAR images yield a large number of false alarms and misses. However, the proposed algorithm can improve both the efficiency and accuracy of target detection in a complicated environment and under weak-target conditions. The experimental results validate the performance of our method.

[1]  H. Andrews,et al.  Singular Value Decomposition (SVD) Image Coding , 1976, IEEE Trans. Commun..

[2]  J. F. Kalaska,et al.  Attention in hierarchical models of object recognition , 2007 .

[3]  Laurent Itti,et al.  An Integrated Model of Top-Down and Bottom-Up Attention for Optimizing Detection Speed , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[4]  R. Desimone,et al.  Neural mechanisms of selective visual attention. , 1995, Annual review of neuroscience.

[5]  I. Rybak,et al.  A model of attention-guided visual perception and recognition , 1998, Vision Research.

[6]  Martin D. Levine,et al.  Visual information processing in primate cone pathways. I. A model , 1996, IEEE Trans. Syst. Man Cybern. Part B.

[7]  Laurent Itti,et al.  Saliency and Gist Features for Target Detection in Satellite Images , 2011, IEEE Transactions on Image Processing.

[8]  Yiming Pi,et al.  Multiphase SAR Image Segmentation With $G^{0}$ -Statistical-Model-Based Active Contours , 2013, IEEE Transactions on Geoscience and Remote Sensing.

[9]  Alan Chalmers,et al.  Detail to Attention: Exploiting Visual Tasks for Selective Rendering , 2003, Rendering Techniques.

[10]  Ali Borji,et al.  Exploiting local and global patch rarities for saliency detection , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[11]  Hoi-Jun Yoo,et al.  Familiarity based unified visual attention model for fast and robust object recognition , 2010, Pattern Recognit..

[12]  Ioannis Rigas,et al.  Low-Level Visual Saliency With Application on Aerial Imagery , 2013, IEEE Geoscience and Remote Sensing Letters.

[13]  Pietro Perona,et al.  Selective visual attention enables learning and recognition of multiple objects in cluttered scenes , 2005, Comput. Vis. Image Underst..

[14]  Lei Yang,et al.  Variance WIE based infrared images processing , 2006 .

[15]  John K. Tsotsos,et al.  Attending to visual motion , 2005, Comput. Vis. Image Underst..

[16]  Dirk Walther,et al.  Interactions of visual attention and object recognition : computational modeling, algorithms, and psychophysics. , 2006 .

[17]  C. Koch,et al.  Computational modelling of visual attention , 2001, Nature Reviews Neuroscience.

[18]  A. Treisman,et al.  A feature-integration theory of attention , 1980, Cognitive Psychology.

[19]  Christof Koch,et al.  A Model of Saliency-Based Visual Attention for Rapid Scene Analysis , 2009 .

[20]  R. Ruotolo,et al.  USING SVD TO DETECT DAMAGE IN STRUCTURES WITH DIFFERENT OPERATIONAL CONDITIONS , 1999 .

[21]  Thomas B. Moeslund,et al.  Long-Term Occupancy Analysis Using Graph-Based Optimisation in Thermal Imagery , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[22]  Mei Tian,et al.  An investigation into using singular value decomposition as a method of image compression , 2005, 2005 International Conference on Machine Learning and Cybernetics.