基于邻域显著性的可见光和SAR遥感图像海面舰船协同检测方法

张强; 王志豪; 王学谦; 李刚; 黄立威; 宋慧娜; 宋朝晖

doi:10.12000/JR24037

基于邻域显著性的可见光和SAR遥感图像海面舰船协同检测方法

doi: 10.12000/JR24037

1.
清华大学电子工程系北京 100084
2.
北京市遥感信息研究所北京 100854
3.
杭州电子科技大学浙江省空间信息感知与传输重点实验室杭州 310018

基金项目: 国家重点研发计划(2021YFA0715201)，国家自然科学基金(62101303, 62341130)，清华大学电子工程系自主科研计划

详细信息

作者简介:
张　强，硕士生，主要研究方向为遥感图像处理、目标检测

王志豪，博士生，主要研究方向为遥感图像处理、变化检测、多源信息融合

王学谦，博士，助理教授，博士生导师，主要研究方向为多源信息融合、遥感图像处理

李　刚，博士，教授，博士生导师，主要研究方向为雷达信号处理、稀疏信号处理、分布式信号处理、多源信息融合

黄立威，博士，工程师，主要研究方向为图像处理、机器学习

宋慧娜，博士，副教授，硕士生导师，主要研究方向为深度学习模型、图像解译、InSAR数据处理

宋朝晖，博士，教授，博士生导师，主要研究方向为电磁场与微波技术、空间信息感知与处理、空间信息网络与传输

通讯作者:
王学谦 wangxueqian@mail.tsinghua.edu.cn

责任主编：王智睿 Corresponding Editor: WANG Zhirui
中图分类号: TN957.52
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出版历程
- 收稿日期: 2024-03-12
- 修回日期: 2024-05-11
- 网络出版日期: 2024-06-07

Cooperative Detection of Ships in Optical and SAR Remote Sensing Images Based on Neighborhood Saliency

1.
Department of Electronic Engineering, Tsinghua University, Beijing 100084, China
2.
Beijing Institute of Remote Sensing, Beijing 100854, China
3.
Zhejiang Key Laboratory of Space Information Sensing and Transmission, Hangzhou Dianzi University, Hangzhou 310018, China

Funds: The National Key R&D Program of China (2021YFA0715201), The National Natural Science Foundation of China (62101303, 62341130), Autonomous Research Program of the Department of Electronic Engineering, Tsinghua University

More Information

Corresponding author: WANG Xueqian, wangxueqian@mail.tsinghua.edu.cn

摘要

摘要: 在遥感图像舰船检测任务中，可见光图像细节和纹理信息丰富，但成像质量易受云雾干扰，合成孔径雷达(SAR)图像具有全天时和全天候的特点，但图像质量易受复杂海杂波影响。结合可见光和SAR图像优势的协同检测方法可以提高舰船目标的检测性能。针对在前后时相图像中，舰船目标在极小邻域范围内发生轻微偏移的场景，该文提出一种基于邻域显著性的可见光和SAR多源异质遥感图像舰船协同检测方法。首先，通过可见光和SAR的协同海陆分割降低陆地区域的干扰，并通过RetinaNet和YOLOv5s分别进行可见光和SAR图像的单源目标初步检测；其次，提出了基于单源检测结果对遥感图像邻域开窗进行邻域显著性目标二次检测的多源协同舰船目标检测策略，实现可见光和SAR异质图像的优势互补，减少舰船目标漏检、虚警以提升检测性能。在2022年烟台地区拍摄的可见光和SAR遥感图像数据上，该方法的检测精度AP50相比现有舰船检测方法提升了1.9%以上，验证了所提方法的有效性和先进性。
- 可见光遥感 /
- 合成孔径雷达(SAR) /
- 舰船目标 /
- 多源协同 /
- 邻域显著性
Abstract: In ship detection through remote sensing images, optical images often provide rich details and texture information; however, the quality of such optical images can be affected by cloud and fog interferences. In contrast, Synthetic Aperture Radar (SAR) provides all-weather and all-day imaging capabilities; however, SAR images are susceptible to interference from complex sea clutter. Cooperative ship detection combining the advantages of optical and SAR images can enhance the detection performance of ships. In this paper, by focusing on the slight shift of ships in a small neighborhood range in the prior and later temporal images, we propose a method for cooperative ship detection based on neighborhood saliency in multisource heterogeneous remote sensing images, including optical and SAR data. Initially, a sea-land segmentation algorithm of optical and SAR images is applied to reduce interference from land regions. Next, single-source ship detection from optical and SAR images is performed using the RetinaNet and YOLOv5s models, respectively. Then, we introduce a multisource cooperative ship target detection strategy based on the neighborhood window opening of single-source detection results in remote sensing images and secondary detection of neighborhood salient ships. This strategy further leverages the complementary advantages of both optical and SAR heterogeneous images, reducing the possibility of missing ship and false alarms to improve overall detection performance. The performance of the proposed method has been validated using optical and SAR remote sensing data measured from Yantai, China, in 2022. Compared with existing ship detection methods, our method improves detection accuracy AP50 by ≥1.9%, demonstrating its effectiveness and superiority.
- Optical remote sensing /
- Synthetic Aperture Radar (SAR) /
- Ships /
- Multisource cooperation /
- Neighborhood saliency

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图 1 多源异质舰船协同检测难点分析

Figure 1. Challenges of cooperative detection of multisource heterogeneous ships

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图 2 基于邻域显著性的可见光和SAR遥感图像海面舰船协同检测流程

Figure 2. Flowchart of cooperative detection of ships in optical and SAR remote sensing images based on neighborhood saliency

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图 3 舰船目标空间偏移示意图

Figure 3. Example of spatial shift of ships

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图 4 有舰船目标时的邻域子图及多源异质遥感图像协同检测处理中间结果

Figure 4. Neighborhood subgraphs including ships and intermediate results of cooperative detection of multisource

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图 5 最大对称环绕视觉示意图

Figure 5. Schematic diagram of maximum symmetrical surround

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图 6 内部框融合示意图

Figure 6. Illustration of inner rectangle box fusion

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图 7 后时相SAR图像邻域子图及其显著性检测形态学处理中间结果

Figure 7. Neighborhood subgraphs of later temporal SAR images and their morphological processing intermediate results for saliency detection

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图 8 无舰船目标时邻域子图及多源异质遥感图像协同检测处理中间结果

Figure 8. Neighborhood subgraphs excluding ships and intermediate results of cooperative detection of multisource heterogeneous remote sensing images

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图 9 (近似)共时相多源异质遥感图像

Figure 9. Approximately co-observed multisource heterogeneous remote sensing images

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图 10 DOTA数据集与本文可见光数据舰船目标尺寸分布图

Figure 10. Size distribution of ships in DOTA dataset and optical images used in this paper

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图 11 SRSDD-v1.0数据集与本文SAR数据舰船目标尺寸分布图

Figure 11. Size distribution of ships in SRSDD-v1.0 dataset and SAR images used in this paper

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图 12 海陆分割结果(白色区域是指预测并真属于陆地区域、黄色区域是预测属于陆地但实际属于海面区域、黑色区域是预测并真属于海面区域、红色区域是预测属于海面但实际属于陆地区域)

Figure 12. Sea-land segmentation results (white regions indicate predicted and true land, yellow regions indicate predicted land but sea, black regions indicate predicted and true sea, and red regions indicate predicted sea)

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图 13 开窗倍数设置与检测性能之间的关系

Figure 13. The relationship between windowing multiplication setting and detection performance

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图 14 (近似)共时相多源异质遥感数据5倍开窗示意图(舰船目标的真实位置用红色框标出，黄色框为以可见光图像中舰船目标为中心的5倍邻域开窗结果)

Figure 14. 5-fold windowing of approximately co-observed multisource heterogeneous remote sensing images (the real position of the ship is marked with a red box, while the yellow box represents the 5-fold windowing result centered on the ship in optical images)

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图 15 不同检测方法检测PR曲线比较

Figure 15. Comparison of PR curves for detection methods

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图 16 多源异质图像协同检测示例图

Figure 16. Example of cooperative detection of multisource heterogeneous images

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表 1 单源检测器性能对比(%)

Table 1. Performance comparison of single-source detectors (%)

图像类型	方法	P	R	F1	AP50
可见光	RetinaNet	95.7	91.5	93.6	95.3
	YOLOv5s	81.5	75.8	78.6	77.1
	YOLOv5m	81.6	77.1	79.3	79.7
	YOLOv5l	70.6	65.9	68.2	58.6
	YOLOv5x	67.7	65.4	66.5	58.6
SAR	RetinaNet	50.2	39.6	44.3	38.5
	YOLOv5s	62.6	50.0	55.6	50.7
	YOLOv5m	60.0	38.9	47.2	48.6
	YOLOv5l	54.6	37.5	44.5	42.5
	YOLOv5x	40.1	33.3	36.4	31.4

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1 基于前时相SAR单源检测结果辅助的后时相可见光图像显著性舰船目标检测算法

1. Algorithm for detecting salient ships in later temporal optical images assisted by prior temporal SAR single-source detection results

输入：SAR单源检测结果框
$ {B^{\rm S}} = \{ [b_1^{\rm S},c_1^{\rm S}],[b_2^{\rm S},c_2^{\rm S}], \cdots ,[b_m^{\rm S},c_m^{\rm S}], \cdots ,[b_M^{\rm S},c_M^{\rm S}]\} $，其中，$ b_m^{\rm S} $代　表SAR单源检测结果中第m个检测结果框，$ c_m^{\rm S} $代表相应检测框　的置信度。
输出：后时相可见光图像检测结果　$ {B^{\rm{OC}}} = \{ {B^{\rm O}},B_1^{\rm{OC}},B_2^{\rm{OC}}, \cdots ,B_M^{\rm{OC}}\} $。
步骤：
1. 根据$ b_m^{\rm S} $生成可见光图像相应区域的邻域子图$ {\boldsymbol I}_m^{\rm O} $，并转换到　CIELAB色彩空间得到转换后的邻域子图$ {\boldsymbol I}_m^{\rm{Lab}} $；
2. 对$ {\boldsymbol I}_m^{\rm{Lab}} $进行高斯滤波获得平滑图像$ {\boldsymbol I}_m^{{\mathrm{Lab}},s} $；
3. 按式(5)对$ {\boldsymbol I}_m^{\rm{Lab}} $计算均值图像$ {\boldsymbol I}_m^{{\mathrm{Lab}},\mu } $；
4. 将步骤2和步骤3中的结果代入式(4)计算$ {\boldsymbol I}_m^{\rm{Lab}} $对应的显著性图　$ {\boldsymbol{S}}_m^{\rm{Lab}} $；
5. 利用OTSU方法分割$ {\boldsymbol{S}}_m^{\rm{Lab}} $获得所有可能的前景目标区域$ {T_m} $；
6. 计算前景目标区域面积，剔除$ {T_m} $中小的区块噪声和异常大连　通区域杂波；
7. 按式(8)，根据$ c_m^{\rm S} $计算步骤6中$ {T_m} $的各个前景目标区域置信度　得分和最大外接矩形检测框，得到当前邻域子图检测结果$ B_m^{\rm{OC}} $；
8. 重复步骤1—步骤7直到遍历SAR单源检测结果框$ {B^{\rm S}} $中的M个　检测框，获得共M组邻域子图检测结果，再与可见光单源检测结　果$ {B^{\rm O}} $合并，生成前时相SAR单源检测结果辅助后时相可见光　图像检测结果$ {B^{\rm{OC}}} = \{ {B^{\rm O}},B_1^{\rm{OC}},B_2^{\rm{OC}}, \cdots ,B_M^{\rm{OC}}\} $；
9. 对$ {B^{\rm{OC}}} $进行内部框融合和NMS。

下载: 导出CSV

2 基于前时相可见光单源检测结果辅助的后时相SAR图像显著性舰船目标检测算法

2. Algorithm for detecting salient ships in later temporal SAR images assisted by prior temporal optical single-source detection results

输入：可见光单源检测结果框
$ {B^{\rm O}} = \{ [b_1^{\rm O},c_1^{\rm O}],[b_2^{\rm O},c_2^{\rm O}], \cdots,[b_n^{\rm O},c_n^{\rm O}], \cdots,[b_N^{\rm O},c_N^{\rm O}]\} $，其中，$ b_n^{\rm O} $代　表可见光单源检测结果中第n个检测结果框，$ c_n^{\rm O} $代表相应检测框　的置信度。
输出：后时相SAR图像检测结果　$ {B^{\rm{SC}}} = \{ {B^{\rm{SC}}},B_1^{\rm{SC}},B_2^{\rm{SC}},\cdots,B_N^{\rm{SC}}\} $
步骤：
1. 根据$ b_n^{\rm O} $生成SAR图像相应区域邻域子图$ {\boldsymbol I}_n^{\rm S} $；
2. 对$ {\boldsymbol I}_n^{\rm S} $进行高斯滤波获得平滑图像$ {\boldsymbol I}_n^{{\mathrm{S}},s} $；
3. 按式(5)对$ {\boldsymbol I}_n^{\rm S} $计算均值图$ {\boldsymbol I}_n^{{\mathrm{S}},\mu } $；
4. 将步骤2和步骤3中的结果代入式(4)计算$ {\boldsymbol I}_n^{\rm S} $对应的显著性图　$ {\boldsymbol{S}}_n^{\rm S} $；
5. 利用OTSU方法分割$ {\boldsymbol{S}}_n^{\rm S} $获得所有可能的前景目标区域$ T_n^{\rm S} $；
6. 计算前景目标区域面积，剔除$ T_n^{\rm S} $中小的区块噪声和异常大连　通区域杂波；
7. 对步骤6处理后的$ T_n^{\rm S} $进行形态学处理，使得弱关联性的单体目　标的多个散射区域连通完整；
8. 按式(8)根据$ c_n^{\rm O} $计算步骤7中$ T_n^{\rm S} $的各个前景目标区域置信度得　分，以及最大外接矩形检测框，得到当前邻域子图检测框$ B_n^{\rm{SC}} $；
9. 重复步骤1—步骤8直到遍历可见光单源检测结果框$ {B^{\rm O}} $中N个　检测框，获得共N组邻域子图检测结果框，与SAR单源检测结果　框$ {B^{\rm S}} $合并，生成前时相可见光单源检测结果辅助后时相SAR 　图像检测结果$ {B^{\rm{SC}}} = \{ {B^{\rm{SC}}},B_1^{\rm{SC}},B_2^{\rm{SC}},\cdots,B_N^{\rm{SC}}\} $；
10. 对$ {B^{\rm{SC}}} $进行内部框融合和NMS。

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表 2 海陆分割性能比较(%)

Table 2. Performance comparison of sea-land segmentation methods (%)

图像类型	方法	PL	RL	LF1	AccL
可见光	OTSU	92.32	71.10	80.33	87.96
可见光	K-means	98.98	70.13	82.09	89.42
SAR	OTSU	94.44	74.57	83.33	89.69
SAR	K-means	99.04	70.06	82.07	89.42
可见光+SAR 协同	OTSU+OTSU	93.07	89.12	91.05	93.94
	K-means+K-means	94.66	89.82	92.18	94.73
	本文方法	94.20	90.93	92.54	94.93

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表 3 不同检测方法基于可见光图像(SAR图像辅助)和SAR图像(可见光图像辅助)的检测性能比较(%)

Table 3. Performance comparison of detection methods based on optical images (assisted by SAR images) and SAR images (assisted by optical images) (%)

图像类型	方法	P	R	F1	AP50
可见光 (后时相)	RetinaNet	95.7	91.5	93.6	95.3
	FusionDet	56.5	77.1	65.2	50.1
	MFDet	72.5	85.6	78.5	74.4
	本文方法 (前时相SAR图像辅助)	94.7	94.1	94.4	97.2
SAR (后时相)	YOLOv5s	62.6	50.0	55.6	50.7
	FusionDet	51.6	43.1	46.9	44.7
	MFDet	68.6	41.7	51.8	49.7
	本文方法 (前时相可见光图像辅助)	67.5	72.3	69.8	66.9

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表 4 海陆分割以及多源异质遥感图像协同检测步骤的消融实验(%)

Table 4. Ablation study on sea-land segmentation and cooperative detection of multisource heterogeneous remote sensing images (%)

图像类型	海陆分割	多源异质遥感图像协同检测	P	R	F1	AP50
可见光 (后时相)	–	–	95.7	91.5	93.6	95.3
	√	–	99.0	92.2	95.5	96.5 (+1.2)
	–	√	93.4	94.1	93.8	96.6 (+1.3)
	√	√	94.7	94.1	94.4	97.2 (+1.9)
SAR (后时相)	–	–	62.6	50.0	55.6	50.7
	√	–	73.6	51.4	60.5	58.5 (+7.8)
	–	√	58.3	68.1	62.8	59.1(+8.4)
	√	√	67.5	72.3	69.8	66.9 (+16.2)
注：括号内容表示相对基准方法性能提升结果。

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表 5 单源检测器的消融实验(%)

Table 5. Ablation study on single-source detectors (%)

图像类型	可见光图像单源检测器	SAR图像单源检测器	P	R	F1	AP50
可见光 (后时相)	RetinaNet	RetinaNet	84.9	93.5	90.0	92.6
	YOLOv5s	YOLOv5s	84.2	80.1	82.1	78.9
	YOLOv5s	RetinaNet	84.3	77.1	80.5	79.5
	RetinaNet	YOLOv5s	94.7	94.1	94.4	97.2
SAR (后时相)	RetinaNet	RetinaNet	61.6	74.3	67.4	61.6
	YOLOv5s	YOLOv5s	70.3	69.2	69.8	65.9
	YOLOv5s	RetinaNet	67.4	70.4	68.9	62.6
	RetinaNet	YOLOv5s	67.5	72.3	69.8	66.9

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