A Hybrid Features Based Detection Method for Inshore Ship Targets in SAR Imagery

doi:10.11947/j.JGGS.2023.0108

Abstract

Abstract:

Convolutional Neural Networks (CNNs) have recently attracted much attention in the ship detection from Synthetic Aperture Radar (SAR) images. However, compared with optical images, SAR ones are hard to understand. Moreover, due to the high similarity between the man-made targets near shore and inshore ships, the classical methods are unable to achieve effective detection of inshore ships. To mitigate the influence of onshore ship-like objects, this paper proposes an inshore ship detection method in SAR images by using hybrid features. Firstly, the sea-land segmentation is applied in the pre-processing to exclude obvious land regions from SAR images. Then, a CNN model is designed to extract deep features for identifying potential ship targets in both inshore and offshore water. On this basis, the high-energy point number of amplitude spectrum is further introduced as an important and delicate feature to suppress false alarms left. Finally, to verify the effectiveness of the proposed method, numerical and comparative studies are carried out in experiments on Sentinel-1 SAR images.

Key words: Convolutional Neural Network (CNN); Synthetic Aperture Radar (SAR); inshore ship detection; hybrid features; high-energy point number; amplitude spectrum

Tong ZHENG,Peng LEI,Jun WANG. A Hybrid Features Based Detection Method for Inshore Ship Targets in SAR Imagery[J]. Journal of Geodesy and Geoinformation Science, 2023, 6(1): 95-107.

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Cases	Configuration	Precision	Recall	FoM	F₁
1	3@3_3@4_3@3	52.03	76.63	44.90	61.98
2	3@5_3@5_3@4	36.75	86.68	34.79	51.62
3	3@7_3@6_3@3	43.46	78.53	38.84	55.95
4	3@11_3@6_3@3	31.36	89.13	30.20	46.39
5	4@3_4@4_4@3	56.58	66.58	44.06	61.17
6	6@3_6@4_6@3	57.46	63.86	43.36	60.49
7	8@3_8@4_8@3	54.64	70.38	44.43	61.52
8	16@3_16@4_16@3	42.84	89.40	40.77	57.92
9	3@7_3@6_3@3_3@2	28.23	93.21	27.66	43.44
10	3@11_3@6_3@3_3@2	31.57	92.39	30.77	47.06
11	3@11_3@8_3@3_3@2	28.87	93.75	28.33	44.15

Results	Precision	Recall	FoM	F₁
(a) Subset of HRSID(%)
Pre+CFAR	29.77	31.74	30.20	30.72
Pre+CNN	25.76	26.12	24.22	25.94
Faster R-CNN	27.12	25.05	23.21	26.04
Proposed	35.53	32.16	31.88	33.76
(b) Subset of SSDD(%)
Results	Precision	Recall	FoM	F₁
Pre+CFAR	30.77	29.74	28.22	30.25
Pre+CNN	29.12	30.45	27.18	30.11
Faster R-CNN	26.50	28.05	25.21	27.25
Proposed	31.22	30.12	29.17	30.66