融合边缘与全局特征的遥感影像显著性目标检测方法

doi:10.11947/j.AGCS.2025.20240247

测绘学报 ›› 2025, Vol. 54 ›› Issue (7): 1265-1279.doi: 10.11947/j.AGCS.2025.20240247

融合边缘与全局特征的遥感影像显著性目标检测方法

谢亚坤¹^,²(), 赵耀纪¹, 涂佳星¹, 夏瑞丰¹, 冯德俊¹, 刘苏凝¹, 陈虹宇¹, 朱军¹()

^1.西南交通大学地球科学与工程学院，四川　成都　611756
^2.西南交通大学桥梁智能与绿色建造全国重点实验室，四川　成都　611756

收稿日期:2024-06-19 修回日期:2025-06-06 出版日期:2025-08-18 发布日期:2025-08-18
通讯作者: 朱军 E-mail:yakunxie@163.com;zhujun@swjtu.edu.cn
作者简介:谢亚坤（1991—），男，博士，副教授，研究方向为信息智能感知与数字孪生建模。E-mail：yakunxie@163.com
基金资助:
国家自然科学基金(42301473);中国博士后创新人才支持计划(BX20230299);中国博士后科学基金(2023M742884);四川省自然科学基金(24NSFSC2264);四川省重点研发项目(24ZDYF0633)

Edge and global features integrated network for salient object detection in optical remote sensing images

Yakun XIE¹^,²(), Yaoji ZHAO¹, Jiaxing TU¹, Ruifeng XIA¹, Dejun FENG¹, Suning LIU¹, Hongyu CHEN¹, Jun ZHU¹()

^1.Faculty of Geosciences and Engineering, Southwest Jiaotong University, Chengdu 611756, China
^2.State Key Laboratory of Bridge Intelligent and Green Construction, Southwest Jiaotong University, Chengdu 611756, China

Received:2024-06-19 Revised:2025-06-06 Online:2025-08-18 Published:2025-08-18
Contact: Jun ZHU E-mail:yakunxie@163.com;zhujun@swjtu.edu.cn
About author:XIE Yakun (1991—), male, PhD, associate professor, majors in intelligent information perception and digital twin modeling. E-mail: yakunxie@163.com
Supported by:
The National Natural Science Foundation of China(42301473);China Postdoctoral Innovation Talents Support Program(BX20230299);China Postdoctoral Science Foundation(2023M742884);Natural Science Foundation of Sichuan Province(24NSFSC2264);Key Research and Development Project of Sichuan Province(24ZDYF0633)

摘要/Abstract

摘要：

遥感影像显著性检测（SOD）能有效区分影像中的关键特征和区域，从而提升图像分析的精确度和处理效率。然而，由于遥感影像的复杂性，现有遥感影像SOD方法存在显著性目标定位不准、边界模糊、目标置信度弱等问题。为解决这些问题，本文提出了一种融合边缘与全局信息的遥感影像显著性目标检测方法。首先，设计了边缘特征增强模块，利用Sobel算子提取浅层特征图中的边缘信息，生成边界线索特征图，并融合边界注意力和空间、通道注意力，进一步增强局部特征表示，从而有效改善显著目标的边界模糊问题。然后，提出了全局上下文特征增强模块，通过全局平均池化和全连接层获取图像级语义信息，并结合空间注意力机制生成全局关联图，并以此为基础，利用多尺度注意力和上下文特征增强策略，提升显著目标的置信度和定位准确性。最后，为验证本文方法的有效性，在ORSSD数据集、EORSSD数据集及ORSI-4199数据集上进行了试验分析，分别降低了0.001 3~0.120 5、0.001~0.159 3和0.003 5~0.136 7，S_α分别提高了0.005 7~0.266 3、0.003~0.336 6和0.013 9~0.240 3，F_β分别提高了0.031 4~0.339 1、0.023 2~0.517 8和0.004 3~0.328 9。结果表明，本文方法在检测精度和效率方面均显著优于现有方法，且能够有效应对遥感影像中的复杂场景和多变条件。

关键词: 遥感影像, 显著性目标检测, 边缘特征, 全局信息

Abstract:

Salient object detection in remote sensing images (SOD) effectively distinguishes key features and regions within images, thus enhancing the precision and efficiency of image analysis. However, due to the complexity of remote sensing images, existing remote sensing images SOD methods suffer from issues such as inaccurate target localization, blurred boundaries, and weak target confidence. To address these challenges, this paper proposes a novel method for remote sensing images SOD that integrates edge and global information. Initially, an edge feature enhancement module is designed, utilizing the Sobel operator to extract edge information from shallow feature maps to generate boundary clue feature maps. These are integrated with boundary attention and spatial-channel attention to further enhance local feature representation, effectively mitigating the issue of blurred salient object boundaries. Secondly, a global context feature enhancement module is introduced, acquiring image-level semantic information through global average pooling and fully connected layers, and combining it with spatial attention mechanisms to generate global association maps. Based on this, the multi-scale attention and context feature enhancement strategies are employed to improve the confidence and localization accuracy of salient objects. Finally, to validate the effectiveness of the proposed method, this paper conducted experimental analysis on three ORSSD datasets, the EORSSD dataset, and the ORSI4199 dataset. The scores decreased by 0.001 3~0.120 5, 0.001~0.159 3, and 0.003 5~0.136 7, respectively. The S_α scores increased by 0.005 7~0.266 3, 0.003~0.336 6, and 0.013 9~0.240 3, respectively. The F_β scores increased by 0.031 4~0.339 1, 0.023 2~0.517 8, and 0.004 3~0.328 9, respectively. The results demonstrate that the proposed method significantly outperforms existing methods in detection accuracy and efficiency, effectively handling complex scenes and variable conditions in remote sensing images.

Key words: remote sensing images, salient object detection, edge feature, global context

中图分类号:

P237

谢亚坤, 赵耀纪, 涂佳星, 夏瑞丰, 冯德俊, 刘苏凝, 陈虹宇, 朱军. 融合边缘与全局特征的遥感影像显著性目标检测方法[J]. 测绘学报, 2025, 54(7): 1265-1279.

Yakun XIE, Yaoji ZHAO, Jiaxing TU, Ruifeng XIA, Dejun FENG, Suning LIU, Hongyu CHEN, Jun ZHU. Edge and global features integrated network for salient object detection in optical remote sensing images[J]. Acta Geodaetica et Cartographica Sinica, 2025, 54(7): 1265-1279.

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方法	↓	S_α↑	F_β↑
DSG	0.102 4	0.718 6	0.609 0
RCNN	0.127 7	0.684 9	0.559 1
CorrNet	0.009 8	0.938 0	0.912 9
EMFINet	0.010 9	0.936 6	0.900 2
ERPNet	0.013 5	0.925 4	0.897 4
SeaNet	0.010 5	0.926 0	0.894 2
SRAL	0.010 5	0.930 5	0.916 7
AESINet	0.008 5	0.945 5	0.916 0
本文方法	0.007 2	0.951 2	0.948 1

方法	↓	S_α↑	F_β↑
DSG	0.125 0	0.642 3	0.523 3
RCNN	0.164 7	0.601 1	0.401 8
CorrNet	0.008 3	0.928 9	0.877 8
EMFINet	0.008 4	0.929 0	0.872 0
ERPNet	0.008 2	0.921 0	0.863 2
SeaNet	0.008 3	0.920 8	0.864 9
SRAL	0.006 7	0.923 4	0.896 4
AESINet	0.006 4	0.934 7	0.879 2
本文方法	0.005 4	0.937 7	0.919 6

方法	↓	S_α↑	F_β↑
DSG	0.129 4	0.697 1	0.627 0
RCNN	0.163 7	0.649 1	0.548 0
CorrNet	0.036 6	0.862 3	0.856 0
EMFINet	0.037 6	0.866 4	0.848 6
ERPNet	0.030 5	0.863 3	0.843 6
SeaNet	0.030 8	0.872 2	0.865 3
SRAL	0.032 1	0.873 5	0.857 6
AESINet	0.030 5	0.875 5	0.872 6
本文方法	0.027 0	0.889 4	0.876 9

方法	ORSSD			EORSSD			ORSI4199
方法	↓	S_α↑	F_β↑	↓	S_α↑	F_β↑	↓	S_α↑	F_β↑
ASTT	0.009 0	0.935 0	0.906 0	0.006 0	0.925 0	0.874 0	0.029 0	0.883 0	0.869 0
BAFSNet	0.007 9	0.938 5	0.913 6	0.006 0	0.928 6	0.870 8	—	—	—
BANet	0.009 7	0.943 2	0.920 5	0.008 9	0.929 2	0.905 5	0.031 4	0.876 7	0.857 6
CIFNet	0.008 4	0.944 5	0.918 3	0.006 1	0.934 2	0.884 3	—	—	—
CRNet	0.009 1	0.938 9	0.910 7	0.006 3	0.937 0	0.887 3	0.033 5	0.866 9	0.866 9
IP2GRNet	0.009 0	0.940 6	—	0.007 1	0.924 8	—	—	—	—
PCDNet	0.009 4	0.944 4	0.913 8	0.006 9	0.934 6	0.883 0	—	—	—
GLGCNet	0.007 1	0.948 8	0.923 6	0.005 5	0.937 5	0.892 4	0.027 4	0.883 9	0.880 8
本文方法	0.007 2	0.951 2	0.948 1	0.005 4	0.937 7	0.919 6	0.027 0	0.889 4	0.876 9

方法	BSO	CS	CSO	ISO	LCS	MSO	NSO	OC	SSO	均值
CorrNet	0.859 7	0.872 5	0.831 1	0.821 4	0.838 9	0.869 1	0.878 7	0.862 9	0.849 5	0.853 8
EMFINet	0.918 6	0.901 3	0.908 4	0.907 2	0.830 7	0.851 8	0.839 9	0.831 5	0.818 1	0.867 5
ERPNet	0.906 3	0.889 3	0.888 1	0.890 4	0.835 1	0.862 1	0.867 7	0.814 2	0.818 6	0.863 5
SeaNet	0.879 2	0.884 6	0.860 3	0.857 6	0.848 6	0.862 5	0.887 6	0.854 7	0.835 7	0.863 4
AESINet	0.912 8	0.907 6	0.910 2	0.904 4	0.847 9	0.874 5	0.884 7	0.850 6	0.852 5	0.882 8
本文方法	0.920 4	0.914 1	0.917 5	0.905 7	0.852 9	0.879 3	0.886 4	0.878 4	0.869 1	0.891 5

融合边缘与全局特征的遥感影像显著性目标检测方法

Edge and global features integrated network for salient object detection in optical remote sensing images

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图/表 20

参考文献 38

相关文章 15

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Metrics

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方法	Param/MB	FLOPs/GB	FPS
CorrNet	4.07	40.31	38.56
SeaNet	2.75	2.71	98.71
EMFINet	95.09	334.52	15.32
ERPNet	56.48	215.45	34.89
AESINet	51.57	39.19	9.11
本文方法	29.11	15.10	48.79

试验	Baseline	EE		GC	ORSI-4199
试验	Baseline	ED	EH	GC	↓	S_α↑	F_β↑
试验1	√				0.034 3	0.868 2	0.858 7
试验2	√	√	√		0.031 9	0.872 5	0.863 6
试验3	√			√	0.032 1	0.869 4	0.862 0
试验4	√		√	√	0.030 8	0.880 8	0.872 6
试验5	√	√	√	√	0.027 0	0.889 4	0.876 9

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