Ship detection and classification based on cascaded detection of hull and wake from optical satellite remote sensing imagery

Figures & data

Table 1. Technical specifications for the GF-1 sensor.

Band	Spectral range	Spatial resolution	Revisit time	Local time of descending node	Breadth
Panchromatic	450–900 nm	2 m	4 day at the soonest	10:30 AM	60 km
Blue	450–520 nm	8 m
Green	520–590 nm
Red	630–690 nm
NIR	770–890 nm

Figure 1. Some ship examples in different bands of the GF-1 imagery.

Figure 2. Flow chart of cascaded detection of ship hull and wake in this study.

Figure 3. The process of hull refining module.

Table 2. Minimum and maximum of hull shape features for real ships.

Shape feature	Minimum	Maximum
Area	60 m	60000 m^2
Length	20 m	600 m
Width	10 m	100 m
Length-width ratio	1.1	8

Figure 4. Simulated images with the presence of striped wakes (a) and natural waves (c). The corresponding images after Fourier transform are shown in (b) and (d).

Figure 5. Flow chart for striped wake detection.

Table 3. Hull and wake features for different kinds of ships.

Ship category	Hull feature	Wake feature
Fishing vessel	Short	Only turbulent wake appears
Motorboat	Very short	A turbulent wake and two bright narrow V-shape Kelvin arms appear simultaneously
Cargo ship	Long, low length-width ratio	A turbulent wake and 0–2 classical Kelvin arms exist, striped wake and internal wave may present
Warship	Long and thin, high length-width ratio	A turbulent wake, 0–2 classical Kelvin arms and complete striped wake exist at cruising speed and narrow V-shape Kelvin arms appear at full speed

Table 4. Probability that a ship was classified as one of the categories based on hull and wake features.

Feature	Fishing vessel	Motorboat	Cargo ship	Warship
L^a<=30 m	0.5	0.5	0	0
L>30 m	0	0.2	0.4	0.4
L/W^a<=4	0.4	0.3	0.3	0
L/W>4	0	0	0.2	0.8
Classical Kelvin arm	0	0	0.4	0.6
Narrow V-shape arms	0	0.8	0	0.2
Striped wake	0	0	0.2	0.8
Internal wave	0	0	1	0

^aL and W denote length and width of ship hull, respectively.

Figure 6. The saliency detection and hull refining results of ship hulls.

Figure 7. Ship detection and classification results for different scenario. The green polygons were boundaries of ship hulls. Blue and white lines demonstrate dark and bright linear wakes, respectively.

Table 5. Comparison between the performance for the method proposed in this study and state-of-the-art methods.

Method	R	P	F	A	S
Shi et al. (2014)	75.6%	46.1%	0.57	93.9%	94.9%
Yang, Xu, and Li (2017)	75.0%	42.9%	0.55	93.2%	94.3%
Dong, Liu, and Fang (2018)	80.8%	65.3%	0.72	96.6%	97.5%
Nie et al. (2020)	61.5%	21.3%	0.32	85.5%	86.9%
This study without wake	88.5%	78.6%	0.83	98.1%	98.6%
This study	90.1%	88.1%	0.89	98.8%	99.3%

Figure 8. Statistics of the length, length-width ratio and area of targets identified as true ships.

Table 6. Classification results of ships with detected wakes.

Ship category	Manual statistics	Correct classification number	False classification number	Omission number
Fishing vessel	3	3	0	0
Motorboat	3	1	1	1
Cargo ship	91	77	0	14
Warship	2	2	0	0

Table 7. Summarization of invariant texture feature and classifiers.

Texture feature	MI, LBP, RGT, RCD, SURF, KAZE
Classifier	ANN, SVM, RF, GP

Table 8. The optimal texture feature combinations for different classifiers.

Classifier	Texture feature	F
ANN	RGH+RCD+KAZE	0.843
SVM	LBP+RGH+RCD+SURF	0.813
RF	RGH+RCD+KAZE	0.857
GP	LBP+RCD+KAZE	0.862

Figure 9. The minimum IoU of all samples with the change of T_SI.

Shi, Z., X. Yu, Z. Jiang, and B. Li. 2014. “Ship Detection in High-Resolution Optical Imagery Based on Anomaly Detector and Local Shape Feature.” IEEE Transactions on Geoscience and Remote Sensing 52 (8): 4511–4523. doi:10.1109/TGRS.2013.2282355.

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Yang, F., Q. Xu, and B. Li. 2017. “Ship Detection from Optical Satellite Images Based on Saliency Segmentation and Structure-LBP Feature.” IEEE Geoscience and Remote Sensing Letters 14 (5): 602–606. doi:10.1109/LGRS.2017.2664118.

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Dong, C., J. Liu, and X. Fang. 2018. “Ship Detection in Optical Remote Sensing Images Based on Saliency and a Rotation-Invariant Descriptor.” Remote Sensing 10 (3): 400. doi:10.3390/rs10030400.

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Nie, T., X. Han, H. Bin, L. Xiansheng, H. Liu, and B. Guoling. 2020. “Ship Detection in Panchromatic Optical Remote Sensing Images Based on Visual Saliency and Multi-Dimensional Feature Description.” Remote Sensing 12 (1): 152. doi:10.3390/rs12010152.

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Data availability statement

The data that support the findings of this study are from Guangdong Data and Application Center for High-resolution Earth Observation System at http://gdgf.gd.gov.cn/GDGF_Portal/index.jsp.