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Frontiers of Optoelectronics

Front. Optoelectron.    2018, Vol. 11 Issue (3) : 275-284
Detection of small ship targets from an optical remote sensing image
Mingzhu SONG1,2, Hongsong QU1(), Guixiang ZHANG1, Guang JIN1
1. Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China
2. The University of the Chinese Academy of Sciences, Beijing 100049, China
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Detection of small ships from an optical remote sensing image plays an essential role in military and civilian fields. However, it becomes more difficult if noise dominates. To solve this issue, a method based on a low-level vision model is proposed in this paper. A global channel, high-frequency channel, and low-frequency channel are introduced before applying discrete wavelet transform, and the improved extended contrast sensitivity function is constructed by self-adaptive center-surround contrast energy and a proposed function. The saliency image is achieved by the three-channel process after inverse discrete wavelet transform, whose coefficients are weighted by the improved extended contrast sensitivity function. Experimental results show that the proposed method outperforms all competing methods with higher precision, higher recall, and higher F-score, which are 100.00%, 90.59%, and 97.96%, respectively. Furthermore, our method is robust against noise and has great potential for providing more accurate target detection in engineering applications.

Keywords small target      saliency      contrast sensitivity     
Corresponding Authors: Hongsong QU   
Just Accepted Date: 19 March 2018   Online First Date: 04 April 2018    Issue Date: 31 August 2018
 Cite this article:   
Mingzhu SONG,Hongsong QU,Guixiang ZHANG, et al. Detection of small ship targets from an optical remote sensing image[J]. Front. Optoelectron., 2018, 11(3): 275-284.
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Mingzhu SONG
Hongsong QU
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Guang JIN
Fig.1  Flow chart of the proposed method. WT: wavelet transform; WT-1: inverse wavelet transform
Fig.2  Curve of center-surround contrast energy
Fig.3  Image of the center-surround contrast energy. (a) Before change; (b) after change
Fig.4  Our ECSF mode
Fig.5  Contrast of saliency maps using different methods for noise-free image and noise image, from left to right: original, CA, COV, SR, SWD, SIM, MCS, USD, OURS. (a) Noise-free image; (b) impulse; (c) Gaussian; (d) Poisson; (e) multiple (Top row: Typical image1. Bottom row: Typical image2)
Fig.6  Performance of different methods for noise-free images
Fig.7  Performances of different methods for noise image. (a) Impulse; (b) Gaussian; (c) Poisson; (d) multiple
Fig.8  Noise images (Top row) and saliency maps (Bottom row) of (a) Test 1 and (b) Test 2. Noise variance of both of them from left to right: 0, 0.005, 0.010, 0.015, 0.020
Fig.9  Detection results of Test 1 and Test 2
variance precision recall F-score
0 95.59% 86.67% 93.66%
0.005 96.83% 89.71% 95.32%
0.010 90.91% 87.72% 90.25%
0.015 91.38% 89.83% 91.07%
0.020 86.44% 89.47% 87.03%
Tab.1  Detection results of Test 2 (contrast human eyes)
Fig.10  Detection results of imaging test of (a) first group and (b) second group. Top row: original images; bottom row: saliency maps
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