Detection of small ship targets from an optical remote sensing image

doi:10.1007/s12200-018-0744-x

Front. Optoelectron.

2018, Vol. 11

Issue (3) : 275-284 https://doi.org/10.1007/s12200-018-0744-x

RESEARCH ARTICLE

Detection of small ship targets from an optical remote sensing image

Mingzhu SONG^1,², 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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Abstract

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 Author(s): 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.

URL:

https://academic.hep.com.cn/foe/EN/10.1007/s12200-018-0744-x
https://academic.hep.com.cn/foe/EN/Y2018/V11/I3/275

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

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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