Automatic Detection Method of Ships Based on Shortwave Infrared Remote Sensing Images

Songze Bao; Xing Zhong; Ruifei Zhu; Shuhai Yu; Ye Yu; Lanmin Li

doi:10.3788/AOS201838.0528001

Journals >Acta Optica Sinica >Volume 38 >Issue 5 >Page 0528001 > Article

Acta Optica Sinica
Vol. 38, Issue 5, 0528001 (2018)

Automatic Detection Method of Ships Based on Shortwave Infrared Remote Sensing Images

Songze Bao^1、2, Xing Zhong^1、2、*, Ruifei Zhu^1、2, Shuhai Yu¹, Ye Yu^1、2, and Lanmin Li¹

Author Affiliations

¹ Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, Jilin 130033, China

¹ Key Laboratory of Satellite Remote Sensing Application Technology of Jilin Province, Chang Guang Satellite Technology Co., Ltd., Changchun, Jilin 130102, China

¹ Shandong Institute of Space Electronic Technology, China Academy of Space Technology, Yantai, Shandong 264670, China

² Key Laboratory of Satellite Remote Sensing Application Technology of Jilin Province, Chang Guang Satellite Technology Co., Ltd., Changchun, Jilin 130102, China

² University of Chinese Academy of Sciences, Beijing 100049, China

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DOI: 10.3788/AOS201838.0528001 Cite this Article Set citation alerts

Songze Bao, Xing Zhong, Ruifei Zhu, Shuhai Yu, Ye Yu, Lanmin Li. Automatic Detection Method of Ships Based on Shortwave Infrared Remote Sensing Images[J]. Acta Optica Sinica, 2018, 38(5): 0528001 Copy Citation Text

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Fig. 1. Flow chart of automatic detection method of ships based on SWIR images

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Fig. 2. (a) Scene image; (b) reflectance curves of typical features

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Fig. 3. (a) Intensity image I; (b) contrast stretched image I'; (c) land mask; (d) land mask filled in holes; (e) water mask

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Fig. 4. Saliency detection and target chip extraction. (a)(f) Input SWIR image; (b)(g) visible image of same scene (targets marked manually); (c)(h) saliency image; (d)(i) mask image of candidate targets; (e)(j) SWIR image chip of candidate targets

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Fig. 5. Rotation and translation of main axis of ships by Radon transform

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Fig. 6. Gray-scale distribution characteristics of target chips. (a) Schematic of direction and regional segmentation; (b) image in polar coordinate; (c) gray-scale distribution curve; (d) gray-scale distribution histogram

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Fig. 7. S-HOG descriptor. (a) Regional segmentation; (b) histogram of oriented gradient of region B1 in Fig. 7(a)

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Fig. 8. Geometric characterization of target chips. (a) Prescreened target chips; (b) grayscale distribution curve; (c) grayscale distribution histogram; (d) gradient oriented histogram

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Fig. 9. Detection results comparison of visible image and SWIR image. (a)(e) Input images; (b)(f) saliency map under CSM model; (c)(g) results of saliency segmentation; (d)(h) results of target discrimination

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Fig. 10. Ship detection results in various scenes by proposed method

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Fig. 11. Effect of parameter value on detection performance. (a) Accuracy index versus δ; (b) Re for each constraint factor; (c) fDR for each constraint factor

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Input	N_t	N_tt	N_f	P /%	$\begin{matrix} {f_{DR}}_{} \end{matrix}$ /%	$\begin{matrix} {R_{e}}_{} \end{matrix}$ /%	$\begin{matrix} {F_{1}}_{} \end{matrix}$ /%
Visible	142	131	7	94.93	5.07	92.25	93.57
SWIR	142	138	8	94.52	5.48	97.18	95.83

Table 1. Detection results for different input images

Method	N_t	N_tt	N_f	P /%	$\begin{matrix} {f_{DR}}_{} \end{matrix}$ /%	$\begin{matrix} {R_{e}}_{} \end{matrix}$ /%	$\begin{matrix} {F_{1}}_{} \end{matrix}$ /%
Proposed method	142	131	7	94.93	5.07	92.25	93.57
Method in [5]	142	116	35	76.82	23.18	81.69	79.18
Method in [12]	142	121	4	96.80	3.20	85.21	90.64

Table 2. Detection results for different methods

Songze Bao, Xing Zhong, Ruifei Zhu, Shuhai Yu, Ye Yu, Lanmin Li. Automatic Detection Method of Ships Based on Shortwave Infrared Remote Sensing Images[J]. Acta Optica Sinica, 2018, 38(5): 0528001

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