A method combining the siamese inverted residual structure with self-attention enhancement for change detection based on remote sensing images

doi:10.6046/zrzyyg.2023388

Abstract
Figures/Tables
References
Related Articles
Metrics

Download: PDF(6137 KB) HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks

Abstract

Change detection based on remote sensing images holds significant application potential in land source survey updating, and urban development monitoring and planning. Concerning the challenges of change detection based on remote sensing images in practical applications, this study proposed a lightweight change detection method combining the siameseinverted residual structure with self-attention enhancement. Instead of the traditional convolutional neural network structure, the siamese improved inverted residual structure was used as the backbone network to fully extract the feature information and significantly reduce the network complexity. The self-attention enhancement module was employed to improve the network's ability to pay attention to global information. Edge weights were added to the loss function to precisely optimize the details of the extraction results. The multilevel hopping residual connections were applied to fully integrate the global and local features. Finally, the performance of the proposed method was tested on the public and prepared datasets of remote sensing images for change detection, respectively. The results indicate that compared to other change detection methods, the proposed method significantly reduced network parameters and computational complexity while maintaining the detection accuracy, contributing to lightweight models of change detection based on remote sensing images.

Keywords remote sensing image change detection modified inverted residual structure self-attention enhancement module lightweight model

ZTFLH:

TP79

Issue Date: 01 July 2025

	Service

	E-mail this article
	E-mail Alert
	RSS
	Articles by authors

	Qiao ZHANG
	Zhicheng CAO
	Yang SHEN
	Zhoufeng WANG
	Chengwu WANG
	Jiaxin XU

Cite this article:

Qiao ZHANG,Zhicheng CAO,Yang SHEN, et al. A method combining the siamese inverted residual structure with self-attention enhancement for change detection based on remote sensing images[J]. Remote Sensing for Natural Resources, 2025, 37(3): 85-94.

URL:

https://www.gtzyyg.com/EN/10.6046/zrzyyg.2023388 OR https://www.gtzyyg.com/EN/Y2025/V37/I3/85

Fig.1 Framework of the Siam-MViT model

Fig.2 Improved inverse residual module

Fig.3 Self-attention enhancement module

Fig.4 Principle of lightweight Transformer

Fig.5 Positive sample edge extraction

Fig.6 Data enhancement

Tab.1 Comparison of accuracy evaluation of different model on LEVIR-CD and SAMPLE-CD(%)

Tab.2 Visualization results of change detection on LEVIR-CD dataset

Tab.3 Visualization results of change detection on SAMPLE-CD dataset

Fig.7 Comparison of large scale prediction results between Siam-MViT and Changeformer on the SAMPLE-CD dataset

Tab.4 Model calculation efficiency statistics

Tab.5 Ablation experimental results

[1]	李德仁. 利用遥感影像进行变化检测[J]. 武汉大学学报(信息科学版), 2003, 28(s1):7-12.
[1]	Li D R. Change detection using remote sensing images[J]. Geomatics and Information Science of Wuhan University, 2003, 28(s1):7-12.
[2]	Huang F H, Yu Y, Feng T H. Automatic building change image quality assessment in high resolution remote sensing based on deep learning[J]. Journal of Visual Communication and Image Representation, 2019,63:102585.
[3]	Bruzzone L, Bovolo F. A novel framework for the design of change-detection systems for very-high-resolution remote sensing images[J]. Proceedings of the IEEE, 2013, 101(3):609-630.
[4]	眭海刚, 冯文卿, 李文卓, 等. 多时相遥感影像变化检测方法综述[J]. 武汉大学学报(信息科学版), 2018, 43(12):1885-1898.
[4]	Sui H G, Feng W Q, Li W Z, et al. Review of change detection methods for multi-temporal remote sensing imagery[J]. Geomatics and Information Science of Wuhan University, 2018, 43(12):1885-1898.
[5]	任秋如, 杨文忠, 汪传建, 等. 遥感影像变化检测综述[J]. 计算机应用, 2021, 41(8):2294-2305. doi: 10.11772/j.issn.1001-9081.2020101632
[5]	Ren Q R, Yang W Z, Wang C J, et al. Review of remote sensing image change detection[J]. Journal of Computer Applications, 2021, 41(8):2294-2305. doi: 10.11772/j.issn.1001-9081.2020101632
[6]	Yool S R, Rogan J. Mapping fire-induced vegetation depletion in the PeloncilloMountains,Arizona and New Mexico[J]. International Journal of Remote Sensing, 2001, 22(16):3101-3121.
[7]	Chen J, Chen X, Cui X, et al. Change vector analysis in posterior probability space:A new method for land cover change detection[J]. IEEE Geoscience and Remote Sensing Letters, 2011, 8(2):317-321.
[8]	Ridd M K, Liu J. A comparison of four algorithms for change detection in an urban environment[J]. Remote Sensing of Environment, 1998, 63(2):95-100.
[9]	Nielsen A A. The regularized iteratively reweighted MAD method for change detection in multi- and hyperspectral data[J]. IEEE Transactions on Image Processing:A Publication of the IEEE Signal Processing Society, 2007, 16(2):463-478.
[10]	Hussain M, Chen D, Cheng A, et al. Change detection from remotely sensed images:From pixel-based to object-based approaches[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2013,80:91-106.
[11]	刘春亭, 冯权泷, 刘建涛, 等. DeepLabv3+语义分割模型的济南市防尘绿网提取及时空变化分析[J]. 遥感学报, 2022, 26(12):2518-2530.
[11]	Liu C T, Feng Q L, Liu J T, et al. Urban green plastic cover extraction and spatial pattern changes in Jinan City based on DeepLabv3+semantic segmentation model[J]. National Remote Sensing Bulletin, 2022, 26(12):2518-2530.
[12]	沙苗苗, 李宇, 李安. 改进Faster R-CNN的遥感图像多尺度飞机目标检测[J]. 遥感学报, 2022, 26(8):1624-1635.
[12]	Sha M M, Li Y, Li A. Multiscale aircraft detection in optical remote sensing imagery based on advanced Faster R-CNN[J]. National Remote Sensing Bulletin, 2022, 26(8):1624-1635.
[13]	郑宗生, 刘海霞, 王振华, 等. 改进3D-CNN的高光谱图像地物分类方法[J]. 自然资源遥感, 2023, 35(2):105-111.doi: 10.6046/zrzyyg.2022100.
[13]	Zheng Z S, Liu H X, Wang Z H, et al. Improved 3D-CNN-based method for surface feature classification using hyperspectral images[J]. Remote Sensing for Natural Resources, 2023, 35(2):105-111.doi: 10.6046/zrzyyg.2022100.
[14]	Daudt C R, LeSaux B, Boulch A. Fully convolutional Siamese networks for change detection. 2018 25th IEEE International Conference on Image Processing (ICIP).Athens,Greece.IEEE, 2018:4063-4067.
[15]	袁洲, 郭海涛, 卢俊, 等. 融合UNet++网络和注意力机制的高分辨率遥感影像变化检测算法[J]. 测绘科学技术学报, 2021, 38(2):155-159.
[15]	Yuan Z, Guo H T, Lu J, et al. High-resolution remote sensing image change detection technology based on UNet++ and attention mechanism[J]. Journal of Geomatics Science and Technology, 2021, 38(2):155-159.
[16]	吴纹辉, 慎利, 董新丰, 等. 面向高分辨率遥感影像建筑物变化检测的边缘感知网络[J]. 地理与地理信息科学, 2021, 37(3):21-28.
[16]	Wu W H, Shen L, Dong X F, et al. Edge sensing network for building change detection in high resolution remote sensing images[J]. Geography and Geo-Information Science, 2021, 37(3):21-28.
[17]	Chen H, Shi Z. A spatial-temporal attention-based method and a new dataset for remote sensing image change detection[J]. Remote Sensing, 2020, 12(10):1662.
[18]	Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need[C]// Proceedings of the 31st International Conference on Neural Information Processing Systems.Long Beach,California,USA.ACM, 2017:6000-6010.
[19]	Dosovitskiy A, Beyer L, Kolesnikov A, et al. An image is worth 16x16 words:Transformers for image recognition at scale[EB/OL]. arXiv, 2010. http://arxiv.org/abs/2010.11929. url: http://arxiv.org/abs/2010.11929
[20]	Chen H, Qi Z, Shi Z. Remote sensing image change detection with transformers[J]. IEEE Transactions on Geoscience and Remote Sensing, 1802,60:5607514.
[21]	Bandara W G C, Patel V M. A Transformer-based Siamese network for change detection[C]// IGARSS 2022-2022 IEEE International Geoscience and Remote Sensing Symposium.Kuala Lumpur,Malaysia. IEEE, 2022:207-210.
[22]	田永林, 王雨桐, 王建功, 等. 视觉Transformer研究的关键问题:现状及展望[J]. 自动化学报, 2022, 48(4):957-979.
[22]	Tian Y L, Wang Y T, Wang J G, et al. Key problems and progress of vision transformers:The state of the art and prospects[J]. ActaAutomatica Sinica, 2022, 48(4):957-979.
[23]	薛白, 王懿哲, 刘书含, 等. 基于孪生注意力网络的高分辨率遥感影像变化检测[J]. 自然资源遥感, 2022, 34(1):61-66.doi: 10.6046/zrzyyg.2021122.
[23]	Xue B, Wang Y Z, Liu S H, et al. Change detection of high-resolution remote sensing images based on Siamese network[J]. Remote Sensing for Natural Resources, 2022, 34(1):61-66.doi: 10.6046/zrzyyg.2021122.
[24]	Zhang X, Zhou X, Lin M, et al. ShuffleNet:An extremely efficient convolutional neural network for mobile devices[C]// 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.Salt Lake City,UT,USA.IEEE, 2018:6848-6856.
[25]	Ma N N, Zhang X Y, Zheng H T, et al. ShuffleNet V2:Practical guidelines for efficient CNN architecture design[C]// Computer Vision-ECCV 2018:15th European Conference,Munich,Germany,2018,Proceedings,Part XIV.ACM, 2018:122-138.
[26]	Sandler M, Howard A, Zhu M, et al. MobileNetV2:Inverted residuals and linear bottlenecks[C]// 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.Salt Lake City,UT,USA.IEEE, 2018:4510-4520.
[27]	Tan M, Le Q V. EfficientNet:Rethinking model scaling for convolutional neural networks[J/OL]. arXiv, 2020. http://arxiv.org/abs/1905.11946v5. url: http://arxiv.org/abs/1905.11946v5
[28]	Mehta S, Rastegari M. MobileViT:Light-weight,general-purpose,and mobile-friendly vision transformer[J/OL]. arXiv, 2021. http://arxiv,2022.org/abs/2110.02178. url: http://arxiv,2022.org/abs/2110.02178
[29]	Chen J, Yuan Z, Peng J, et al. DASNet:Dual attentive fully convolutional Siamese networks for change detection in high-resolution satellite images[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021,14:1194-1206.
[30]	Ghiasi G, Cui Y, Srinivas A, et al. Simple copy-paste is a strong data augmentation method for instance segmentation[C]// 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).Nashville,TN,USA.IEEE, 2021:2917-2927.
[31]	Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks[J]. Communications of the ACM, 2017, 60(6):84-90.
[32]	王振庆, 周艺, 王世新, 等. IEU-Net高分辨率遥感影像房屋建筑物提取[J]. 遥感学报, 2021, 25(11):2245-2254.
[32]	Wang Z Q, Zhou Y, Wang S X, et al. House building extraction from high-resolution remote sensing images based on IEU-Net[J]. National Remote Sensing Bulletin, 2021, 25(11):2245-2254.
[33]	王子浩, 李轶鲲, 李小军, 等. 基于空间模糊C均值聚类和贝叶斯网络的抗噪声遥感图像变化检测[J]. 自然资源遥感, 2023, 35(4):96-104.doi: 10.6046/zrzyyg.2022260.
[33]	Wang Z H, Li Y K, Li X J, et al. Noise-resistant change detection for remote sensing images based on spatial fuzzy C-means clustering and a Bayesian network[J]. Remote Sensing for Natural Resources, 2023, 35(4): 96-104. doi: 10.6046/zrzyyg.2022260.

[1]	HE Xiaojun, LUO Jie. Semantic segmentation of high-resolution remote sensing images based on context- and class-aware feature fusion[J]. Remote Sensing for Natural Resources, 2025, 37(2): 1-10.
[2]	PANG Min. An intelligent platform for extracting patches from multisource domestic satellite images and its application[J]. Remote Sensing for Natural Resources, 2025, 37(2): 148-154.
[3]	SHAO Pan, GUAN Zongsheng, JIA Fuwen. Unsupervised change detection using SAR images based on the broad learning system[J]. Remote Sensing for Natural Resources, 2025, 37(2): 19-29.
[4]	NIE Shiyin, LIU Yansong, LI Huiling, XUE Kailun, SHEN Duheng, HE Boyu. Identification and classification of land types of alpine wetlands based on spectral coupling[J]. Remote Sensing for Natural Resources, 2025, 37(2): 204-211.
[5]	DENG Jianming, YAO Hang, FU Bolin, GU Sen, TANG Jie, GAN Yuanyuan. Monitoring the spatiotemporal dynamics of mangrove forests in Beibu Gulf, Guangxi Zhuang Autonomous Region, China, using Google Earth Engine and time-series active and passive remote sensing images[J]. Remote Sensing for Natural Resources, 2025, 37(2): 235-245.
[6]	CHEN Jiaxue, XIAO Dongsheng, CHEN Hongyu. A boundary guidance and cross-scale information interaction network for water body extraction from remote sensing images[J]. Remote Sensing for Natural Resources, 2025, 37(1): 15-23.
[7]	XU Xinyu, LI Xiaojun, GE Junfei, LI Yikun. A multispectral image pansharpening algorithm based on nonsubsampled contourlet transform (NSCT) combined with a guided filter[J]. Remote Sensing for Natural Resources, 2025, 37(1): 24-30.
[8]	WANG Tixin, YANG Jinzhong, XING Yu, WANG Kaijian. A method for automatic mapping of the remote sensing monitoring results of national nature reserves based on ArcPy and map optimization[J]. Remote Sensing for Natural Resources, 2025, 37(1): 252-259.
[9]	QU Haicheng, LIANG Xu. Building extraction from high-resolution images using a hybrid attention mechanism combined with multi-scale feature enhancement[J]. Remote Sensing for Natural Resources, 2024, 36(4): 107-116.
[10]	PAN Junjie, SHEN Li, YAN Xin, NIE Xin, DONG Kuanlin. An adversarial learning-based unsupervised domain adaptation method for semantic segmentation of high-resolution remote sensing images[J]. Remote Sensing for Natural Resources, 2024, 36(4): 149-157.
[11]	ZHAO Jinling, HUANG Jian, LIANG Zijun, ZHAO Xuedan, JIN Tao, GE Hanghang, WEI Xiaoyan, SHAO Yuanzheng. BDANet-based assessment of building damage from earthquake disasters[J]. Remote Sensing for Natural Resources, 2024, 36(4): 193-200.
[12]	TAI Jiayi, SHEN Li, QIAO Wenfan, ZHOU Wuzhen. Impacts of different proportions of contextual information on the construction of sample sets of remote sensing scene images for damaged buildings[J]. Remote Sensing for Natural Resources, 2024, 36(3): 154-162.
[13]	YU Miao, JING Hongbo, WANG Xiang, LI Xingjiu. Multi-directional target detection based on depth features[J]. Remote Sensing for Natural Resources, 2024, 36(3): 267-271.
[14]	WEN Quan, LI Lu, XIONG Li, DU Lei, LIU Qingjie, WEN Qi. A review of water body extraction from remote sensing images based on deep learning[J]. Remote Sensing for Natural Resources, 2024, 36(3): 57-71.
[15]	LIN Dan, LI Qiucen, CHEN Zhikui, ZHONG Fangming, LI Lifang. Research advances and challenges in multi-label classification of remote sensing images[J]. Remote Sensing for Natural Resources, 2024, 36(2): 10-20.

Viewed

Full text

Abstract

Cited

Shared

Discussed