New method for ship detection in synthetic aperture radar imagery based on the human visual attention system

Abstract We propose a new algorithm for ship detection in synthetic aperture radar (SAR) images based on the human visual attention system. The human visual attention system identifies the prominent objects in images or scenes so that these objects can be more noticeable. Since the ships in a SAR image of the ocean are prominent objects, they can easily be identified through the human visual attention system. Thus, for detection of ships in the SAR images, the present study (through its application) has modeled the human visual attention system in the detection stage. In this way, not only can the targets be precisely detected, but also the falsely detected pixels are significantly reduced. Compared to most existing algorithms in the literature, our proposed algorithm can be used for both homogeneous and nonhomogeneous images. Accordingly, its performance is independent of the image type (homogeneous or nonhomogeneous) and the computation time significantly decreases. Experimental results have shown the efficiency of the proposed algorithm for various SAR images from ERS-1, ERS-2, and ALOS PALSAR data.

[1]  G. Ferrara,et al.  A Physically Consistent Speckle Model for Marine SLC SAR Images , 2007, IEEE Journal of Oceanic Engineering.

[2]  Liming Jiang,et al.  Using SAR Images to Detect Ships From Sea Clutter , 2008, IEEE Geoscience and Remote Sensing Letters.

[3]  Liming Zhang,et al.  Spatio-temporal Saliency detection using phase spectrum of quaternion fourier transform , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[4]  Gui Gao,et al.  A Parzen-Window-Kernel-Based CFAR Algorithm for Ship Detection in SAR Images , 2011, IEEE Geoscience and Remote Sensing Letters.

[5]  Tom Fawcett,et al.  An introduction to ROC analysis , 2006, Pattern Recognit. Lett..

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

[7]  Fátima N. S. de Medeiros,et al.  Target Detection in SAR Images Based on a Level Set Approach , 2009, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[8]  Alfonso Farina,et al.  Coherent radar detection in log-normal clutter , 1986 .

[9]  Zhuo Chen.  An Improved Automatic Ship Detection Method in SAR Images , 2009, 2009 2nd International Congress on Image and Signal Processing.

[10]  Weidong Yu,et al.  A New CFAR Ship Detection Algorithm Based on 2-D Joint Log-Normal Distribution in SAR Images , 2010, IEEE Geoscience and Remote Sensing Letters.

[11]  S Ullman,et al.  Shifts in selective visual attention: towards the underlying neural circuitry. , 1985, Human neurobiology.

[12]  Liming Zhang,et al.  Biological Plausibility of Spectral Domain Approach for Spatiotemporal Visual Saliency , 2008, ICONIP.

[13]  Carlos López-Martínez,et al.  Use of the multiresolution capability of wavelets for ship detection in SAR imagery , 2004, IGARSS 2004. 2004 IEEE International Geoscience and Remote Sensing Symposium.

[14]  J.J. Mallorqui,et al.  A novel approach for the automatic detection of punctual isolated targets in a noisy background in SAR imagery , 2005, European Radar Conference, 2005. EURAD 2005..

[15]  M. Migliaccio,et al.  Ship detection over single-look complex SAR images , 2008, 2008 IEEE/OES US/EU-Baltic International Symposium.

[16]  Henry Chan,et al.  Radar sea-clutter at low grazing angles , 1990 .

[17]  R. Rifkin,et al.  Analysis of CFAR performance in Weibull clutter , 1994 .

[18]  S.L. Zhou,et al.  A fast algorithm based on two-stage CFAR for detecting ships in SAR images , 2009, 2009 2nd Asian-Pacific Conference on Synthetic Aperture Radar.

[19]  Carlos López-Martínez,et al.  A novel algorithm for ship detection in SAR imagery based on the wavelet transform , 2005, IEEE Geoscience and Remote Sensing Letters.

[20]  Kazuo Ouchi,et al.  On a Novel Approach Using MLCC and CFAR for the Improvement of Ship Detection by Synthetic Aperture Radar , 2010, IEEE Geoscience and Remote Sensing Letters.

[21]  Vahid Tabataba Vakili,et al.  Introducing excision switching-CFAR in K distributed sea clutter , 2009, Signal Process..

[22]  D. Crisp,et al.  The State-of-the-Art in Ship Detection in Synthetic Aperture Radar Imagery , 2004 .

[23]  Jianqin Zhou,et al.  On discrete cosine transform , 2011, ArXiv.

[24]  Bin Wang,et al.  Pulse discrete cosine transform for saliency-based visual attention , 2009, 2009 IEEE 8th International Conference on Development and Learning.

[25]  Mingsheng Liao,et al.  Ship Detection in SAR Image Based on the Alpha-stable Distribution , 2008, Sensors.

[26]  Ying Yu,et al.  Hebbian-Based Neural Networks for Bottom-Up Visual Attention Systems , 2009, ICONIP.

[27]  Kazuo Ouchi,et al.  Ship detection based on coherence images derived from cross correlation of multilook SAR images , 2004, IEEE Geoscience and Remote Sensing Letters.

[28]  Soo H. Rho,et al.  Double-step fast CFAR scheme for multiple target detection in high resolution SAR images , 2010, 2010 IEEE Radar Conference.

[29]  Zhenhong Du,et al.  A New Method for Ship Detection in SAR Imagery Based on Combinatorial PNN Model , 2008, 2008 First International Conference on Intelligent Networks and Intelligent Systems.

[30]  Bingfang Wu,et al.  A scheme for ship detection in inhomogeneous regions based on segmentation of SAR images , 2008 .

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

[32]  Z. Zlatko Petrov.  Detection of Targets in Foliage Clutter, Based on Multiresolutional Denoising , 2005, EUROCON 2005 - The International Conference on "Computer as a Tool".

[33]  Ridha Touzi,et al.  Calibrated polarimetric SAR data for ship detection , 2000, IGARSS 2000. IEEE 2000 International Geoscience and Remote Sensing Symposium. Taking the Pulse of the Planet: The Role of Remote Sensing in Managing the Environment. Proceedings (Cat. No.00CH37120).

[34]  Liqing Zhang,et al.  Saliency Detection: A Spectral Residual Approach , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.