时空差异增强与自适应特征融合的轻量级遥感影像变化检测网络

doi:10.11947/j.AGCS.2025.20240299

测绘学报 ›› 2025, Vol. 54 ›› Issue (1): 136-153.doi: 10.11947/j.AGCS.2025.20240299

• 摄影测量学与遥感 • 上一篇

时空差异增强与自适应特征融合的轻量级遥感影像变化检测网络

龚良雄¹(), 李星华²(), 程远明³, 赵兴友¹, 谢仁平⁴, 王红根¹

^1.南昌市测绘勘察研究院有限公司，江西　南昌　330038
^2.武汉大学遥感信息工程学院，湖北　武汉　430079
^3.南昌市城市规划设计研究总院集团有限公司，江西　南昌　330038
^4.东莞理工学院计算机科学与技术学院，广东　东莞　523808

收稿日期:2024-07-19 修回日期:2024-12-12 发布日期:2025-02-17
通讯作者: 李星华 E-mail:1021386774@qq.com;lixinghua5540@whu.edu.cn
作者简介:龚良雄（1991—），男，硕士，高级工程师，研究方向为遥感影像智能解译。　E-mail：1021386774@qq.com
基金资助:
国家自然科学基金(42171302)

A lightweight remote sensing images change detection network utilizing spatio-temporal difference enhancement and adaptive feature fusion

Liangxiong GONG¹(), Xinghua LI²(), Yuanming CHENG³, Xingyou ZHAO¹, Renping XIE⁴, Honggen WANG¹

^1.Nanchang Institute of Surveying and Mapping Co., Ltd., Nanchang 330038, China
^2.School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
^3.Nanchang Urban Planning & Design Institute Group Co., Ltd., Nanchang 330038, China
^4.School of Computer Science and Technology, Dongguan University of Technology, Dongguan 523808, China

Received:2024-07-19 Revised:2024-12-12 Published:2025-02-17
Contact: Xinghua LI E-mail:1021386774@qq.com;lixinghua5540@whu.edu.cn
About author:GONG Liangxiong (1991—), male, master, senior engineer, majors in intelligent interpretation of remote sensing imagery.　E-mail: 1021386774@qq.com
Supported by:
The National Natural Science Foundation of China(42171302)

摘要/Abstract

摘要：

针对现有遥感影像变化检测方法存在多时相差异特征利用不足、多尺度特征融合不足等问题，提出一种时空差异增强与自适应特征融合的轻量级遥感影像变化检测网络。本文设计了轻量级时空差异增强模块，采用语义变化感知和空间变化感知的双分支结构，组合利用语义自适应增强机制和混合注意力机制，增强双时相特征图的空谱差异。不同尺度特征图通过边缘细化残差模块进一步优化变化区域边缘。还改进了双向特征融合金字塔结构，采用可学习的权重参数来量化不同尺度特征的重要性，实现多尺度特征的有效融合。选取10种主流的变化检测方法，在WHU-CD、LEVIR-CD、SYSU-CD和SECOND数据集上进行模型对比试验，结果表明：SEAFNet相较于多种主流的变化检测方法，在定性分析、定量分析、网络复杂度与准确度平衡方面均取得了比较优异的表现。

关键词: 遥感影像, 时空差异增强, 注意力机制, 自适应特征融合, 变化检测

Abstract:

To address the limitations in existing change detection methods of remote sensing images, such as insufficient utilization of multi-temporal difference features and inadequate multi-scale feature fusion, a lightweight remote sensing images change detection network named SEAFNet is proposed, which integrates spatio-temporal difference enhancement with adaptive feature fusion. This paper designs the lightweight spatio-temporal difference enhancement module, which employs a dual-branch structure with semantic change perception and spatial change perception. This module combines a semantic adaptive enhancement mechanism and a mixed attention mechanism to enhance the space-spectrum differences in the bi-temporal feature maps. To further refine the edges of the change regions, different scale feature maps are optimized through an edge refinement residual module. The bi-directional feature fusion pyramid structure is also improved by using learnable weight parameters to quantify the importance of features at different scales, achieving effective multi-scale feature fusion. Comparative experiments with ten mainstream change detection methods on WHU-CD, LEVIR-CD, SYSU-CD and SECOND datasets demonstrate that SEAFNet outperforms these methods in qualitative and quantitative analysis, and the balance between network complexity and accuracy.

Key words: remote sensing images, spatio-temporal difference enhancement, attention mechanism, adaptive feature fusion, change detection

中图分类号:

P237

龚良雄, 李星华, 程远明, 赵兴友, 谢仁平, 王红根. 时空差异增强与自适应特征融合的轻量级遥感影像变化检测网络[J]. 测绘学报, 2025, 54(1): 136-153.

Liangxiong GONG, Xinghua LI, Yuanming CHENG, Xingyou ZHAO, Renping XIE, Honggen WANG. A lightweight remote sensing images change detection network utilizing spatio-temporal difference enhancement and adaptive feature fusion[J]. Acta Geodaetica et Cartographica Sinica, 2025, 54(1): 136-153.

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主干网络	主干网络复杂度嵌入后网络复杂度				WHU-CD/(%)			LEVIR-CD/(%)			SYSU-CD/(%)
主干网络	参数量/M	FLOPs/G	参数量/M	FLOPs/G	IoU	F₁值	OA	IoU	F₁值	OA	IoU	F₁值	OA
Res Net18	11.18	19.05	13.44	22.31	77.12	87.08	98.83	78.61	88.02	98.85	67.23	80.40	91.23
Res Net34	21.28	38.43	23.55	41.69	72.08	83.78	98.49	79.53	88.60	98.89	66.69	80.01	91.21
V3-Small	1.14	1.35	1.73	2.71	78.34	87.86	98.92	76.93	86.96	98.75	65.92	79.46	90.99
V3-Large	3.41	5.79	5.67	9.05	80.59	89.25	99.05	81.23	89.65	98.96	69.46	81.97	91.78
Swin-S	48.76	89.13	32.51	51.28	83.04	90.73	99.22	82.96	91.07	99.11	68.27	81.14	91.48
Swin-B	86.64	158.3	95.52	167.01	80.50	89.20	99.04	81.39	89.74	98.97	65.48	79.14	90.52
Repvit_m1_1	7.77	14.37	10.04	17.63	79.44	88.54	98.99	80.33	89.09	98.92	67.27	80.43	91.26
Repvit_m1_5	13.62	24.43	15.88	27.69	75.54	86.06	98.77	78.22	87.78	98.83	64.70	78.57	90.69

损失函数	WHU-CD			LEVIR-CD			SYSU-CD			SECOND
损失函数	IoU	F₁值	OA	IoU	F₁值	OA	IoU	F₁值	OA	IoU	F₁值	OA
BCE loss+Dice loss	78.88	88.19	98.93	80.74	89.35	98.95	69.55	82.04	91.79	58.38	73.72	94.69
Focal loss+Dice loss	80.59	89.25	99.05	81.23	89.65	98.96	69.46	81.97	91.78	58.93	74.16	94.67

网络模型	网络复杂度		WHU-CD/(%)			LEVIR-CD/(%)			SYSU-CD/(%)			SECOND/(%)
网络模型	参数量/M	FLOPs/G	IoU	F₁值	OA	IoU	F₁值	OA	IoU	F₁值	OA	IoU	F₁值	OA
Tiny-CD	0.29	6.18	66.34	79.76	97.91	82.16	90.21	99.02	65.10	78.86	90.31	57.20	72.78	93.95
RFANet	2.86	12.65	73.28	84.58	98.52	81.11	89.57	98.95	65.61	79.24	90.53	56.51	72.22	94.39
FC-Siam-diff	1.35	18.91	64.05	78.09	97.73	73.41	84.66	98.49	65.35	79.04	90.79	50.42	67.04	93.44
SNUNet	12.03	219.33	71.59	83.45	98.40	81.33	89.49	98.96	65.64	79.25	90.71	55.04	71.00	94.00
AMTNet	16.45	58.85	72.93	84.35	98.56	79.29	88.45	98.84	60.89	75.69	88.91	51.08	67.62	93.65
ChangeFormer	29.75	84.73	70.05	82.39	98.28	79.80	88.76	98.87	63.90	77.97	89.40	51.10	67.64	93.20
BIT	11.47	105.24	72.49	84.11	98.45	80.62	89.27	98.92	63.72	77.30	89.18	56.79	72.44	94.45
TFI-GR	27.78	38.96	76.65	86.70	98.78	80.66	89.29	98.96	67.84	81.00	91.34	55.42	71.32	93.99
CDNeXt	39.42	64.33	76.53	86.53	98.73	80.77	89.36	98.94	67.14	80.34	91.16	56.50	72.20	94.29
DMINet	6.24	59.49	78.04	87.76	98.93	80.25	89.04	98.92	67.72	80.75	91.32	57.68	73.16	94.65
SEAFNet	5.67	9.05	80.77	89.36	99.06	82.74	90.73	99.08	70.64	82.76	92.16	59.79	74.83	94.76

分支名称		WHU-CD/(%)			LEVIR-CD/(%)			SYSU-CD/(%)			SECOND/(%)			网络复杂度
分支1	分支2	IoU	F₁值	OA	IoU	F₁值	OA	IoU	F₁值	OA	IoU	F₁值	OA	参数量/M	FLOPs/G
×	×	74.77	85.56	98.77	71.25	83.21	98.49	60.55	75.43	89.92	49.91	66.59	93.42	3.57	8.79
×	√	76.35	86.59	98.76	73.58	84.78	98.59	64.69	78.56	90.67	50.83	67.40	93.82	3.55	8.77
√	×	76.04	86.39	98.88	73.98	85.04	98.45	63.60	77.75	90.49	51.34	67.84	93.49	5.56	9.06
√	√	77.60	87.39	98.93	77.76	87.49	98.72	65.31	79.01	90.69	52.45	68.81	93.69	5.56	9.06

权重参数	WHU-CD/(%)			LEVIR-CD/(%)			SYSU-CD/(%)			SECOND/(%)			网络复杂度
权重参数	IoU	F₁值	OA	IoU	F₁值	OA	IoU	F₁值	OA	IoU	F₁值	OA	参数量/M	FLOPs/G
×	76.69	86.81	98.94	77.29	87.17	98.73	66.40	79.81	91.35	53.88	70.03	94.21	3.67	8.72
√	77.29	87.19	98.90	77.78	87.52	98.74	66.88	80.15	91.31	54.61	70.64	94.28	3.67	8.72

时空差异增强与自适应特征融合的轻量级遥感影像变化检测网络

A lightweight remote sensing images change detection network utilizing spatio-temporal difference enhancement and adaptive feature fusion

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模块名称			WHU-CD/(%)			LEVIR-CD/(%)			SYSU-CD/(%)			SECOND/(%)			网络复杂度
STDEM	ERRM	SAPM	IoU	F₁值	OA	IoU	F₁值	OA	IoU	F₁值	OA	IoU	F₁值	OA	参数量/M	FLOPs/G
√	√	√	80.59	89.25	99.05	81.23	89.65	98.96	69.46	81.97	91.78	58.93	74.16	94.67	5.67	9.05
×	√	√	78.59	88.01	98.92	79.57	88.62	98.89	67.73	80.76	91.52	54.66	70.68	94.35	3.68	8.78
√	×	√	80.43	89.16	99.04	79.87	88.81	98.89	68.57	81.35	91.63	57.17	72.75	94.19	5.67	8.99
√	√	×	78.79	88.14	99.02	78.17	87.75	98.79	65.76	79.35	90.92	53.52	69.72	92.96	5.57	9.13

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