Research Review on Marine Search and Rescue

Locating the marine target in a quick and precise way is the crucial point of implementing SAR (search and rescue) at sea, which involves aspects of developing SAR strategy and detects the marine targets. As the effect of marine target detection restricts the SAR result directly, the study has focused on reviewing the previous research about marine target detection, especially dim marine target detection. What’s more, small target detection under complex sea status is one of the severe challenges which is research’s hotspot and needs more endeavor. Current research results and future research directions are discussed in the paper. The findings can provide systematic view of implementing maritime search and rescue for field researchers and governors.

[1]  K. Pleskacz,et al.  An Adaptation of an Algorithm of Search and Rescue Operations to Ship Manoeuvrability , 2015 .

[2]  H. E. Wensink On parametric detection of small targets in sea clutter , 2000, Proceedings of the Third International Conference on Information Fusion.

[3]  James R. Morrison,et al.  Decision support scheduling for maritime search and rescue planning with a system of UAVs and fuel service stations , 2015, 2015 International Conference on Unmanned Aircraft Systems (ICUAS).

[4]  Hadi Sadoghi Yazdi,et al.  A target-based color space for sea target detection , 2012, Applied Intelligence.

[5]  Chen Qi-shui New detection algorithm for dim IR point targets under complicated sea and sky background , 2005 .

[6]  Ran Xin Saliency detection for sea visual scene using SVD , 2012 .

[7]  Jürgen Schmidhuber,et al.  Deep learning in neural networks: An overview , 2014, Neural Networks.

[8]  Floris Goerlandt,et al.  GIS-based cost distance modelling to support strategic maritime search and rescue planning: A feasibility study , 2015 .

[9]  Julian Lockett Development and Application of a Methodology to assess the Adequacy of Search and Rescue on the River Thames , 2005 .

[10]  John J. Soraghan,et al.  Small-target detection in sea clutter , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[11]  Wai Keung Wong,et al.  Deep Learning Regularized Fisher Mappings , 2011, IEEE Transactions on Neural Networks.

[12]  Jian Zhang,et al.  Multifractal Correlation Characteristic of Real Sea Clutter and Low-Observable Targets Detection: Multifractal Correlation Characteristic of Real Sea Clutter and Low-Observable Targets Detection , 2010 .

[13]  Li Cuihua Novel method for moving maritime objects detection , 2007 .

[14]  Gao Xiao-ying Detection for ship targets in complicated background of sea and land , 2007 .

[15]  P.L. Herselman,et al.  Improved covariance matrix estimation in spectrally inhomogeneous sea clutter with application to adaptive small boat detection , 2008, 2008 International Conference on Radar.

[16]  Wei Ying,et al.  Wavelet analysis based detection algorithm for infrared image small target in background of sea and sky , 2003, 3rd International Symposium on Image and Signal Processing and Analysis, 2003. ISPA 2003. Proceedings of the.

[17]  Jianbo Gao,et al.  Detection of low observable targets within sea clutter by structure function based multifractal analysis , 2006 .

[18]  Hugo Ferreira,et al.  Multiple robot operations for maritime search and rescue in euRathlon 2015 competition , 2016, OCEANS 2016 - Shanghai.

[19]  Peng Jia-xion Infrared Background Suppression for Segmenting and Detecting Small Target , 1999 .

[20]  Zhang Tianxu Knowledge-based detection for small target in rotation sea background , 2007 .

[21]  Ren Lei Small target detection in ocean environment using local and global saliency , 2012 .

[22]  W. Tung,et al.  Modeling sea clutter as a nonstationary and nonextensive random process , 2006, 2006 IEEE Conference on Radar.

[23]  Zou Xuping Target detection algorithm for marine radar based on maximum likelihood estimate , 2012 .

[24]  YU Hai-bin A detection method for IR point target on sea background based on morphology , 2003 .

[25]  Feng De-ying A method for detecting small infrared targets in the sea or sky , 2009 .

[26]  Yu Hai-bin Automatic detection method of IR small target in complex sea background , 2003 .

[27]  YU Hai-bin Wavelet Transform-based Detection for Small IR Target in Complex Sea Background , 2003 .

[28]  Simon Watts,et al.  Detection of small targets in sea clutter , 2006 .

[29]  Xie Xiaozhu,et al.  Effective Method for Moving Objects Detection on Sea Surface , 2008, 2008 International Conference on Computer Science and Software Engineering.

[30]  Xiaolong Chen,et al.  Fractal Feature Discriminant of Sea Clutter in FRFT Domain and Moving Target Detection Algorithm: Fractal Feature Discriminant of Sea Clutter in FRFT Domain and Moving Target Detection Algorithm , 2011 .

[31]  Zhou Zhen Dim Small IR Sea Target Detection Based on Wavelet and Context Model , 2010 .

[32]  Lei Ren,et al.  Architecture of vision enhancement system for maritime search and rescue , 2008, 2008 8th International Conference on ITS Telecommunications.

[33]  Ren Lei Small and weak target detection on sea surface based on visible light video image processing , 2010 .

[34]  Li Wei,et al.  The optimized selection methods of marine search and rescue ships , 2016, 2016 13th International Conference on Service Systems and Service Management (ICSSSM).

[35]  Ran Xin Marine rescue target detection algorithm based on decision tree , 2010 .

[36]  Jing Hu,et al.  TARGET DETECTION WITHIN SEA CLUTTER: A COMPARATIVE STUDY BY FRACTAL SCALING ANALYSES , 2006 .

[37]  Jing Hu,et al.  A New Way to Model Nonstationary Sea Clutter , 2009, IEEE Signal Processing Letters.

[38]  D. Morrell,et al.  A Subspace-Based Approach to Sea Clutter Suppression for Improved Target Detection , 2006, 2006 Fortieth Asilomar Conference on Signals, Systems and Computers.

[39]  David Hyunchul Shim,et al.  Vision-Based Detection and Tracking of Airborne Obstacles in a Cluttered Environment , 2012, J. Intell. Robotic Syst..

[40]  Zheng Zhong DETECTING INFRARED SMALL TARGETS BASED ON ADAPTIVE LOCAL ENERGY THERSHOLD UNDER SEA-SKY COMPLEX BACKGROUNDS , 2006 .

[41]  Amy Loutfi,et al.  A review of unsupervised feature learning and deep learning for time-series modeling , 2014, Pattern Recognit. Lett..