Peng DING, Ye ZHANG, Xu-ling CHANG. Ship detection on sea surface based on multi-feature and multi-scale visual attention[J]. Optics and precision engineering, 2017, 25(9): 2461-2468.
DOI:
Peng DING, Ye ZHANG, Xu-ling CHANG. Ship detection on sea surface based on multi-feature and multi-scale visual attention[J]. Optics and precision engineering, 2017, 25(9): 2461-2468. DOI: 10.3788/OPE.20172509.2461.
Ship detection on sea surface based on multi-feature and multi-scale visual attention
a new method to detect ship targets on sea surface was proposed based on multi-feature and multi-scale visual saliency. Firstly a scale-adaptive top-hat algorithm was used to suppress the interference of clouds and oil. Then
the double-quaternion images are constructed by using double-color spatial features and edge features to detect the saliency of ships. This method makes full use of the double quaternion images
so it can be operated at the same time in a number of channels
and can save operation time to guarantee the characteristics of different scale characteristics. Furthermore
the method also uses the character that the human eye focused on the different targets for image with different sized in implement of the up-down sampling to avoid the leak overlapping in image detection. When the last saliency map is obtained
the ships were segmented to ensure the target location by using the OTSU algorithm
and then the ship target was marked and extracted in the original image. The experiments were analyzed in the several sea conditions. Experimental results show that the algorithm eliminates the interference of cloud
fog and oil pollution and ship targets are detected accurately. With this algorithm
true rate iss 97.73%
and the false alarm rate as low as 3.37%. Compared to other frequency domain saliency detection algorithms in ship detection
this algorithm has obvious advantages.
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references
SONG Z N, SUI H G, WANG Y J. Automatic ship detection for optical satellite images based on visual attention model and LBP [C]. 2014 IEEE Workshop on Electronics, Computer and Applications , IEEE , 2014:722-725. http://ieeexplore.ieee.org/document/6845723/
BORJI A, CHENG M M, JIANG H Z, et al.. Salient object detection:a benchmark[J]. IEEE Transactions on Image Processing, 2015, 24(12):5706-5722.
CHENG M M, MITRA N J, HUANG X L, et al.. Global contrast based salient region detection [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(3):569-582.
KOSMIDOU V E, ADAM A, PAPADANⅡL C D, et al.. Gender effect in human brain responses to bottom-up and top-down attention using the EEG 3D-vector field tomography [C]. 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society ( EMBC ), IEEE , 2015:7574-7577. http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=7320145
BUSO V, GONZALEZ-DÍAZ I, BENOIS-PINEAU J. Object recognition with top-down visual attention modeling for behavioral studies [C]. 2015 IEEE International Conference on Image Processing ( ICIP ), IEEE , 2015:4431-4435. http://ieeexplore.ieee.org/document/7351644/
REN L, SHI C J, RAN X. Salient target detection method under sea surface environment based on multi-scale phase spectrum [C]. 2011 Seventh International Conference on Natural Computation ( ICNC ), IEEE , 2011:977-981. http://ieeexplore.ieee.org/document/6022201/
YAO Z J. Small target detection under the sea using multi-scale spectral residual and maximum symmetric surround [C]. 2013 10 th International Conference on Fuzzy Systems and Knowledge Discovery ( FSKD ), IEEE , 2013:241-245. http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6816200
BIAN P, ZHANG L M. Biological Plausibility of Spectral Domain Approach for Spatiotemporal Visual Saliency [M].KÖPPEN M, KASABOV N, COGHILL G. Advances in Neuro-Information Processing. Lecture Notes in Computer Science. Berlin, Heidelberg:Springer, 2009:251-258.
GUO C L, MA Q, ZHANG L M. Spatio-temporal saliency detection using phase spectrum of quaternion fourier transform [C]. IEEE Conference on Computer Vision and Pattern Recognition , IEEE , 2008:1-8. http://ieeexplore.ieee.org/xpls/icp.jsp?arnumber=4587715
GUO C L, ZHANG L M. A novel multiresolution spatiotemporal saliency detection model and its applications in image and video compression [J]. IEEE Transactions on Image Processing, 2010, 19(1):185-198.
SAID S, LE BIHAN N, SANGWINE S J. Fast complexified quaternion fourier transform [J]. IEEE Transactions on Signal Processing, 2008, 56(4):1522-1531.
BAHADARKHAN K, KHALIQ A A, SHAHID M. A morphological hessian based approach for retinal blood vessels segmentation and denoising using region based otsu thresholding [J]. PLoS One, 2016, 11(7):e0158996.
SHA C S, HOU J, CUI H X, et al.. Gray level-median histogram based 2D otsu's method [C]. 2015 International Conference on Industrial Informatics - Computing Technology , Intelligent Technology , Industrial Information Integration ( ICIICII ), IEEE , 2015:30-33. http://ieeexplore.ieee.org/document/7373783/
BAHADARKHAN K, KHALIQ A A, SHAHID M. A morphological hessian based approach for retinal blood vessels segmentation and denoising using region based otsu thresholding [J]. PLoS One, 2016, 11(7):e0158996.
ELL T A, SANGWINE S J. Hypercomplex fourier transforms of color images [J]. IEEE Transactions on Image Processing, 2007, 16(1):22-35.
JIANGC X, GUO H T, YU J T, et al.. Residential area extraction of high resolution RS imagery based on multiple contour structuring elements with pseudo top-hat transformation [J]. Science of Surveying and Mapping, 2016, 41(3):104-108, 53. (in Chinese)