基于全局差分增强模块和平衡惩罚损失的多源光学遥感影像变化检测

doi:10.11947/j.AGCS.2025.20240300

测绘学报 ›› 2025, Vol. 54 ›› Issue (5): 873-887.doi: 10.11947/j.AGCS.2025.20240300

基于全局差分增强模块和平衡惩罚损失的多源光学遥感影像变化检测

王超¹(), 陈天宇¹, 张同¹, AhmedTanvir¹, 纪立强¹, 谢涛²(), 杨佳俊¹, 王帅¹

^1.南京信息工程大学电子与信息工程学院，江苏　南京　210044
^2.南京信息工程大学遥感与测绘工程学院，江苏　南京　210044

收稿日期:2024-07-22 修回日期:2025-03-20 出版日期:2025-06-23 发布日期:2025-06-23
通讯作者: 谢涛 E-mail:chaowang@nuist.edu.cn;xietao@nuist.edu.cn
作者简介:王超（1984—），男，博士，副教授，研究方向为高分辨率遥感影像处理。E-mail：chaowang@nuist.edu.cn
基金资助:
国家重点研发计划(2022YFC3004202);国家自然科学基金(42176180);安徽省高校杰出青年科研项目(2023AH020022);江苏省产学研项目(BY20230139);江苏省博士后基金(2021K013A)

Multi-sensor optical remote sensing images change detection based on global differential enhancement module and balance penalty loss

Chao WANG¹(), Tianyu CHEN¹, Tong ZHANG¹, Tanvir AHMED¹, Liqiang JI¹, Tao XIE²(), Jiajun YANG¹, Shuai WANG¹

^1.School of Electronics and Information Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China
^2.School of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China

Received:2024-07-22 Revised:2025-03-20 Online:2025-06-23 Published:2025-06-23
Contact: Tao XIE E-mail:chaowang@nuist.edu.cn;xietao@nuist.edu.cn
About author:WANG Chao (1984—), male, PhD, associate professor, majors in high-resolution remote sensing image processing.　E-mail: chaowang@nuist.edu.cn
Supported by:
The National Key Research and Development Program of China(2022YFC3004202);The National Natural Science Foundation of China(42176180);Anhui Province Outstanding Youth Research Project in Universities(2023AH020022);Jiangsu Province Industry-Academia-Research Project(BY20230139);The Post-doctoral Fund of Jiangsu Province(2021K013A)

摘要/Abstract

摘要：

高分辨率光学影像空间细节信息丰富、解译可靠性高，是遥感变化检测任务的主要数据来源之一。在实际应用中，单一来源的光学影像容易受重访周期、存档数据可用性等因素限制而难以满足需求，联合多源光学遥感影像则具有更强的灵活性及适用性。然而，不同传感器获得的光学遥感影像存在较大的时空异质性，显著的“伪不变”和“伪变化”现象给准确提取真实变化信息带来了严峻挑战。为此，本文以开发对“伪不变”和“伪变化”现象兼具强鉴别能力的模型为设计目标，提出了一种基于全局差分增强模块（GDEM）和平衡惩罚损失（BP Loss）的多源光学遥感影像变化检测方法，称为GB-UNet++。其中，GDEM模块通过引入Transformer结构实现多时相影像全局信息间的交互，以增强模型捕捉跨像素/区域变化信息的能力；此外，本文构建的BP Loss能够自适应调整损失的权重，从而提高模型对两类错误样本的学习能力。在6个数据集上进行的大量试验表明，本文方法的总体精度和F₁值分别可达99.02%和84.86%，显著优于5种先进的对比方法。

关键词: 多源光学遥感影像, 变化检测, Transformer, 深度学习

Abstract:

High-resolution optical imagery, characterized by its rich spatial details and high interpretability, is one of the primary data sources for remote sensing change detection tasks. In terms of practical applications, single-source optical imagery is often constrained by revisit periods and the availability of archived data, which may not fully meet the requirements. In contrast, the integration of multi-source optical remote sensing imagery offers greater flexibility and applicability. Nonetheless, the substantial spatiotemporal heterogeneity among optical images acquired by different sensors leads to significant “pseudo-invariance” and “pseudo-change” phenomenon, posing severe challenges to accurately extracting genuine change information. In order to address these challenges, this paper aims to develop a model with strong discriminative capability for addressing both types of false detection. In this paper, a change detection model for multi-sensor optical remote sensing images is proposed, based on global differential enhancement module (GDEM) and balance penalty loss (BP Loss), named GB-UNet++. The GDEM introduces Transformer to facilitate the interaction of global information across multi-temporal images, thereby enhancing the model's ability to capture cross-pixel/region change information. Additionally, the proposed BP Loss adaptively adjusts the weights to enhance the model's capacity to learn from both types of misclassified samples. Extensive experiments on six datasets demonstrate that the proposed method achieves an overall accuracy (OA) of 99.02% and an F₁ score of 84.86%, significantly outperforming five state-of-the-art methods.

Key words: multi-sensor optical remote sensing images, change detection, Transformer, deep learning

中图分类号:

P237

王超, 陈天宇, 张同, AhmedTanvir, 纪立强, 谢涛, 杨佳俊, 王帅. 基于全局差分增强模块和平衡惩罚损失的多源光学遥感影像变化检测[J]. 测绘学报, 2025, 54(5): 873-887.

Chao WANG, Tianyu CHEN, Tong ZHANG, Tanvir AHMED, Liqiang JI, Tao XIE, Jiajun YANG, Shuai WANG. Multi-sensor optical remote sensing images change detection based on global differential enhancement module and balance penalty loss[J]. Acta Geodaetica et Cartographica Sinica, 2025, 54(5): 873-887.

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数据集	数据来源	空间分辨率/m	样本尺寸/像素	时间跨度
BCDD	航拍图像	0.2	256×256	2012—2016年
DSIFN	在线地图服务平台	2	256×256	2001—2019年
GZCD	在线地图服务平台	0.55	256×256	2010—2019年
YCCD	GF-1、GF-2	2	128×128	2018—2020年
NJCD	World View-Ⅱ、航拍图像	2	128×128	2011—2013年
GYCD	GF-1、GF-2	2	128×128	2015—2020年

数据集	方法	P/（%）	R/（%）	PRD/（%）	F₁值/（%）	OA/（%）	Kappa系数
BCDD	Vc T＿CD	79.55	86.42	6.87	82.46	98.86	0.817 2
	BIT＿CD	76.55	85.39	8.84	80.74	98.73	0.806 1
	ChangeFormer	64.97	77.29	12.32	70.60	98.23	0.696 9
	CLNet	74.95	80.61	5.66	77.67	98.59	0.769 4
	MFED-UNet++	81.01	84.63	3.62	82.77	98.89	0.822 1
	GB-UNet++	83.24	86.54	3.30	84.86	99.02	0.843 5
	Vc T＿CD	68.15	67.54	0.61	67.84	88.18	0.638 8
	BIT＿CD	64.05	69.69	5.64	66.75	87.88	0.608 5
DSIFN	ChangeFormer	63.27	65.18	1.91	64.21	86.89	0.609 3
DSIFN	CLNet	75.13	55.62	19.51	63.92	80.05	0.547 9
	MFED-UNet++	55.39	78.28	22.89	64.88	88.15	0.612 2
	GB-UNet++	68.83	69.15	0.32	68.99	88.24	0.666 2
	Vc T＿CD	70.10	74.53	4.43	72.24	93.94	0.688 5
	BIT＿CD	47.58	84.02	36.44	60.75	93.08	0.572 8
GZCD	ChangeFormer	46.51	87.71	41.20	60.78	93.25	0.574 7
GZCD	CLNet	42.70	95.60	52.90	59.03	93.33	0.559 8
	MFED-UNet++	73.10	77.47	4.37	75.22	94.10	0.721 9
	GB-UNet++	75.41	86.12	10.71	80.41	95.87	0.781 2
	Vc T＿CD	52.95	63.63	10.68	57.80	95.60	0.554 6
	BIT＿CD	51.22	63.81	12.59	56.82	95.59	0.555 5
YCCD	ChangeFormer	53.07	52.80	0.27	52.94	94.54	0.500 4
YCCD	CLNet	49.81	63.76	13.95	55.93	95.46	0.535 7
	MFED-UNet++	55.51	65.41	9.90	60.06	95.73	0.578 2
	GB-UNet++	57.31	65.75	8.44	61.24	95.77	0.585 4
	Vc T＿CD	64.48	79.59	15.11	71.21	95.92	0.673 3
	BIT＿CD	65.40	79.87	14.47	71.92	96.37	0.700 1
NJCD	ChangeFormer	62.21	77.86	15.65	69.16	96.06	0.670 9
NJCD	CLNet	64.74	78.53	13.79	70.97	96.24	0.689 8
	MFED-UNet++	66.69	81.33	14.64	73.28	96.55	0.714 6
	GB-UNet++	67.18	81.88	14.70	73.81	96.59	0.720 2
	Vc T＿CD	65.47	72.39	6.92	68.79	91.74	0.658 6
	BIT＿CD	54.18	66.66	12.48	59.78	89.85	0.540 5
GYCD	ChangeFormer	57.51	65.98	8.47	61.46	89.96	0.557 2
GYCD	CLNet	53.95	65.93	11.98	59.34	89.71	0.535 2
	MFED-UNet++	68.66	71.60	2.94	70.09	91.85	0.653 8
	GB-UNet++	68.85	72.53	3.68	70.62	92.04	0.661 1

数据集	基础网络	GDEM	P/（%）	R/（%）	PRD/（%）	F₁值/（%）	OA/（%）	Kappa系数
BCDD	UNet++	—	65.91	63.89	2.02	64.89	97.66	0.636 1
BCDD	UNet++	√	83.24	86.54	3.30	84.86	99.02	0.843 5
DSIFN	UNet++	—	70.57	56.71	13.86	62.89	85.47	0.539 9
DSIFN	UNet++	√	68.83	69.15	0.32	68.99	88.24	0.666 2
GZCD	UNet++	—	56.06	71.56	15.50	62.87	92.55	0.588 1
GZCD	UNet++	√	75.41	86.12	10.71	80.41	95.87	0.781 2
YCCD	UNet++	—	50.77	51.93	1.16	51.35	94.43	0.483 9
YCCD	UNet++	√	57.31	65.75	8.44	61.24	95.77	0.585 4
NJCD	UNet++	—	62.67	80.48	17.81	70.47	96.27	0.685 1
NJCD	UNet++	√	67.18	81.88	14.70	73.81	96.59	0.720 2
GYCD	UNet++	—	57.89	66.99	9.10	62.11	90.16	0.564 9
GYCD	UNet++	√	68.85	72.53	3.68	70.62	92.04	0.661 1

基于全局差分增强模块和平衡惩罚损失的多源光学遥感影像变化检测

Multi-sensor optical remote sensing images change detection based on global differential enhancement module and balance penalty loss

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