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

Acta Optica Sinica, Volume. 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

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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Figures & Tables(13)

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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Table 1. Detection results for different input images
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Table 1. Detection results for different input images
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 2. Detection results for different methods
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Table 2. Detection results for different methods
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

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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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