AIS和光学遥感图像引导的星载SAR舰船目标识别网络

doi:10.7527/S1000-6893.2023.28672

Abstract

Abstract:

Spaceborne SAR is widely used in marine target recognition tasks as an all-season and all-weather sensing means. Due to the low resolution， difficult interpretation and uneven samples of SAR images， the existing single-mode target recognition algorithms have low recognition accuracy. In this paper， a spaceborne SAR ship target recognition network guided by AIS and optical remote sensing images is proposed. To overcome the difficulty caused by different feature dimensions of different modal data， the heterogeneous features are mapped into the common space measurement by using the feature migration module on the premise of preserving the unique feature attributes of each modal. For the problem of sample imbalance in different modes and different categories of data， the heterogeneous feature alignment module is used to fully mine the complementary information of different modes， further align the heterogeneous features of different modes in a fine-grained way， and migrate the discriminant features of each mode as a priori information to SAR image modes. In the experimental part， AIS historical data and optical remote sensing data set are used as auxiliary information on two public SAR image ship target data sets. The experimental results show that the network proposed can effectively improve the recognition accuracy of ship targets in SAR images by fusing different modal information.

Key words: multi-source ship target, remote sensing image, ship target recognition, AIS data, heterogeneous characteristic

CLC Number:

V475.9

Ziling WANG, Zhenyu XIONG, Lucheng YANG, Ruining YANG, Linzhou HUANG. Spaceborne SAR ship target recognition network guided by AIS and optical remote sensing images[J]. Acta Aeronautica et Astronautica Sinica, 2024, 45(2): 328672-328672.

Figures/Tables 8

Fig.1

Table 1

Description of AIS features

特征符号	特征描述	特征定义
$f 1$	长	$L$
$f 2$	宽	$W$
$f 3$	周长（轮廓）	$2 × L + W$
$f 4$	面积（大小）	$L × W$
$f 5$	长宽比率	$L / W$
$f 6$	宽长比率	$W / L$
$f 7$	形状复杂度	$L + W 2 / L × W$
$f 8$	其他特征	$W 2 / L 2 + W 2$
$f 9$		$L - W / L + W$
$f 10$		$L / L + W$
$f 11$		$W / L + W$

Table 1

Fig.2

Table 2

Table 3

Ablation study

数据集	模型	$A R 1$	$A R 2$	$A R 3$	$A R 4$
OpenSARShip	$L Y$	54.31	62.57	61.87	57.34
	+ $L p$	60.21	71.54	72.36	66.33
	+ $L p$ + $L D 1$	65.87	75.88	74.38	67.81
	+ $L p$ + $L D 1$ + $L D 2$	66.78	78.17	77.23	68.54

FUSAR-Ship	$L Y$	62.36	68.58	40.11	61.93
	+ $L p$	70.23	71.36	55.86	71.69
	+ $L p$ + $L D 1$	74.18	75.75	60.23	75.69
	+ $L p$ + $L D 1$ + $L D 2$	76.82	80.69	63.88	79.63

Table 3

Fig.3

Fig.4

Table 4

References 27

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数据集	类别	训练	测试	累计
OpenSARShip	散货船	255	63	318
	货船	518	130	648
	集装箱船	448	112	560
	油船	175	43	218

FUSAR-Ship	散货船	1 281	320	1 601
	货船	1 311	327	1 638
	集装箱船	1 303	325	1 628
	油船	1 351	337	1 688

数据集	数据模态	模型	OA
OpenSARShip	SAR	VGG16^［19］	58.68
		Resnet18^［20］	60.47
		Resnet50^［20］	60.93
	+AIS	MMDT^［23］	45.39
		HFA^［24］	60.87
		CDLS^［25］	61.26
	+OPT	MMDT^［23］	64.98
		HFA^［24］	67.28
		CDLS^［25］	66.56
	+AIS+OPT	DAMA^［26］	64.93
		MUDAF^［27］	67.38
		本文算法	70.08
FUSAR-Ship	SAR	VGG16^［19］	64.38
		Resnet18^［20］	66.11
		Resnet50^［20］	66.92
	+AIS	MMDT^［23］	50.39
		HFA^［24］	66.58
		CDLS^［25］	67.89
	+OPT	MMDT^［23］	69.36
		HFA^［24］	70.75
		CDLS^［25］	70.94
	+AIS+OPT	DAMA^［26］	69.26
		MUDAF^［27］	70.06
		本文算法	74.09

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Spaceborne SAR ship target recognition network guided by AIS and optical remote sensing images

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