融合道路邻近关系的高分遥感目标分割方法

doi:10.11947/j.AGCS.2025.20240276

测绘学报 ›› 2025, Vol. 54 ›› Issue (7): 1294-1304.doi: 10.11947/j.AGCS.2025.20240276

融合道路邻近关系的高分遥感目标分割方法

王朝洋¹(), 苏一少¹, 骆剑承²^,³, 胡晓东⁴, 夏列钢¹^,⁵()

^1.浙江工业大学计算机科学与技术学院，浙江　杭州　310023
^2.中国科学院空天信息创新研究院遥感科学国家重点实验室，北京　100101
^3.中国科学院大学资源与环境学院，北京　100049
^4.浙江科技大学信息与电子工程学院，浙江　杭州　310023
^5.浙江工业大学地理信息学院，浙江　湖州　313299

收稿日期:2024-07-08 修回日期:2025-06-16 出版日期:2025-08-18 发布日期:2025-08-18
通讯作者: 夏列钢 E-mail:wangchaoyly@163.com;xialg@zjut.edu.cn
作者简介:王朝洋（2001—），男，硕士，主要研究方向为遥感图像分割。E-mail：wangchaoyly@163.com

Segmentation method of high-score remote sensing target based on road neighborhood relationship

Chaoyang WANG¹(), Yishao SU¹, Jiancheng LUO²^,³, Xiaodong HU⁴, Liegang XIA¹^,⁵()

^1.School of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China
^2.State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, University of Chinese Academy of Sciences, Beijing 100101, China
^3.College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
^4.School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China
^5.College of Geoinformatics, Zhejiang University of Technology, Huzhou 313299, China

Received:2024-07-08 Revised:2025-06-16 Online:2025-08-18 Published:2025-08-18
Contact: Liegang XIA E-mail:wangchaoyly@163.com;xialg@zjut.edu.cn
About author:WANG Chaoyang (2001—), male, master, majors in remote sensing images segmentation. E-mail: wangchaoyly@163.com

摘要/Abstract

摘要：

近年来，随着深度学习技术的不断发展，遥感影像实例分割实现了在多种数据集上的高效分割结果。然而，现有的遥感影像实例分割方法通常只在像素层面融合空间上下文信息，而忽视了地物目标间的空间关系的挖掘。因此，本文在YOLOv8的基础上提出了融合道路邻近关系的高分遥感目标分割方法，引入了坐标注意力模块和重新设计的距离损失函数，重点关注地物目标间的空间关系，并将其与视觉信息相结合，进一步提升了语义理解和像素级分割精度，显著提高了目标分割的准确性和效率。

关键词: 空间关系, 实例分割, YOLO, 注意力模块, 道路邻近关系

Abstract:

In recent years, the advancement of deep learning technology has been a continuous process. The application of remote sensing image instance segmentation to a variety of datasets has yielded effective and efficient segmentation results. However, existing methods for the instances segmentation of remote sensing image usually only fuse spatial context information at the pixel level, while neglecting the mining of spatial relationships between feature targets. In this paper, we propose a research project on the high-resolution remote sensing target segmentation method fusing road neighborhood relations based on YOLOv8. This method introduces a coordinate attention module and a redesigned distance loss function, which focus on the spatial relations among feature targets and combine them with visual information to enhance the semantic understanding and pixel-level segmentation accuracy. This approach significantly improves the accuracy and efficiency of target segmentation.

Key words: spatial relation, instance segmentation, YOLO, attention module, road neighborhood relations

中图分类号:

P231

王朝洋, 苏一少, 骆剑承, 胡晓东, 夏列钢. 融合道路邻近关系的高分遥感目标分割方法[J]. 测绘学报, 2025, 54(7): 1294-1304.

Chaoyang WANG, Yishao SU, Jiancheng LUO, Xiaodong HU, Liegang XIA. Segmentation method of high-score remote sensing target based on road neighborhood relationship[J]. Acta Geodaetica et Cartographica Sinica, 2025, 54(7): 1294-1304.

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数据集	方法	Tollstation	Servicearea	加油站	Box AP（IoU=0.5）	F₁值
SR	YOLOv8	91.0	92.9	88.1	90.7	87.6
	YOLOv8+HROR-CA	92.4	93.7	87.8	91.3	88.0
	YOLOv8+HROR-Rloss	92.6	92.9	89.7	91.7	89.0
	YOLOv8+HROR-CA+HROR-Rloss	95.2	93.9	88.6	92.6	89.3

数据集	方法	Tollstation	Servicearea	加油站	Mask AP（IoU=0.5）	F₁值
SR	YOLOv8	89.9	94.5	88.2	90.9 8	8.5
	YOLOv8+HROR-CA	91.1	95.4	88.2	91.6 8	9.0
	YOLOv8+HROR-Rloss	92.3	93.7	90.0	92.0 8	9.0
	YOLOv8+HROR-CA+HROR-Rloss	94.9	95.1	89.2	93.0 9	0.0

研究方法	Box AP	Mask AP	Box F₁值	Mask F₁值
Mask R-CNN	82.4	82.1	81.5	81.1
SAM-det	84.0	81.0	82.6	80.8
RSPrompter	82.6	81.2	81.1	80.3
Yolact	85.9	83.3	83.4	82.3
Pointrend	84.2	84.6	82.0	82.8
Solov2	84.1	84.1	83.0	83.0
BoxInst	87.6	87.6	85.2	85.0
SwinTransformer	91.1	91.3	88.0	89.0
HRRSOSN	92.6	93.0	89.3	90.0

研究方法	Box AP	Mask AP	Box F₁值	Mask F₁值
Mask R-CNN	84.0	83.3	83.0	82.8
SAM-det	85.7	85.3	84.9	83.8
RSPrompter	84.8	84.6	83.9	83.3
Yolact	87.5	86.9	86.2	86.3
Pointrend	85.7	85.8	83.5	83.8
Solov2	—	85.2	—	84.9
BoxInst	88.4	88.7	87.3	87.1
SwinTransformer	92.4	92.5	89.6	90.0
HRRSOSN	93.9	94.1	90.4	91.2

研究方法	GFLOPs	Params/MB
Mask R-CNN	155	31.33
Yolact	82.7	50.14
BoxInst	300.9	34.73
SAM-det	127	83.75
RSPrompter	122	86.01
Pointrend	135	60.22
Solov2	34.7	18.27
SwinTransformer	252	69.11
HRRSOSN	12.1	3.28

融合道路邻近关系的高分遥感目标分割方法

Segmentation method of high-score remote sensing target based on road neighborhood relationship

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