A method combining the siamese inverted residual structure with self-attention enhancement for change detection based on remote sensing images
ZHANG Qiao1(), CAO Zhicheng1, SHEN Yang2, WANG Zhoufeng1, WANG Chengwu1, XU Jiaxin1
1. School of Geoscience and Technology, Southwest Petroleum University, Chengdu 610500, China 2. The Third Geodetic Surveying Brigade of MNR, Chengdu 610100, China
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.
张荞, 曹志成, 沈洋, 汪宙峰, 王成武, 许嘉欣. 一种结合孪生倒残差与自注意力增强的遥感影像变化检测方法[J]. 自然资源遥感, 2025, 37(3): 85-94.
ZHANG Qiao, CAO Zhicheng, SHEN Yang, WANG Zhoufeng, WANG Chengwu, XU Jiaxin. A method combining the siamese inverted residual structure with self-attention enhancement for change detection based on remote sensing images. Remote Sensing for Natural Resources, 2025, 37(3): 85-94.
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.
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.
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.
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.
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.
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.
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.
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.
[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.
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.
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.
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.
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.
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.
2025, Vol. 37 
), 曹志成1, 沈洋2, 汪宙峰1, 王成武1, 许嘉欣1
