注意力机制和全局卷积在光伏板分割中的应用

doi:10.3778/j.issn.1002-8331.2209-0180

摘要/Abstract

摘要： 准确识别光伏对光伏产业有效健康发展至关重要。高分辨率遥感图像复杂的背景和光伏板形状颜色多变给光伏识别带来巨大的挑战。针对高分辨率遥感图像中光伏用地提取问题，提出网络以精确地提取光伏用地。该网络采用编码器和解码器的形式融合多层特征以结合丰富的语义信息，利用全局卷积和双注意力机制捕获重要的空间特征和通道特征，并使用通道融合模块恢复丢失的部分通道信息。提出的方法可以有效解决光伏板边缘模糊和光伏板粘连的问题。在公开光伏数据集上的实验表明，与U-Net、SegNet、DeepLabv3和DeepLabv3+相比，所提方法在PV01、PV03、PV08三个数据集上的IoU分别达到87.02%、92.98%和88.43%。实验证明所提方法能对高分辨率遥感图像光伏板进行高准确率分割。

关键词: 高分辨率遥感图像, 光伏用地, 全局卷积, 注意力机制, 语义分割

Abstract: Accurate photovoltaic (PV) identification is critical for the effective and healthy development of PV industry. PV recognition is hampered by the complex background and variable shape and color of PV panels in high-resolution remote sensing images. This paper proposes a method for accurately extracting photovoltaic land from high-resolution remote sensing images. The encoder and decoder functions in this network combine multi-layer features to combine rich semantic data. Important spatial and channel properties are captured using the global convolution and the dual attention mechanism, while some lost channel data are recovered using the channel fusion module. The proposed method can effectively solve the problems of photovoltaic panel blurred edges and adhesion. Experiments on open PV datasets show that the IoU of the proposed method in PV01, PV03, and PV08 is 87.02%, 92.98%, and 88.43%, respectively, when compared to U-Net, SegNet, DeepLabv3, and DeepLabv3+. Experimental results show that the proposed method can achieve high accuracy segmentation of photovoltaic panels in high-resolution remote sensing images.

Key words: high-resolution remote sensing images, photovoltaic land, global convolution, attention mechanism, semantic segmentation

李青, 李海涛, 李辉, 张俊虎. 注意力机制和全局卷积在光伏板分割中的应用[J]. 计算机工程与应用, 2024, 60(4): 237-248.

LI Qing, LI Haitao, LI Hui, ZHANG Junhu. Photovoltaic Panel Segmentation Using Attention Mechanism and Global Convolution[J]. Computer Engineering and Applications, 2024, 60(4): 237-248.

参考文献

[1] KRUITWAGEN L, STORY K T, FRIEDRICH J, et al. A global inventory of photovoltaic solar energy generating units[J]. Nature, 2021, 598(7882): 604-610.
[2] AYOP R, TAN C W, MAHMUD M S A, et al. A simplified and fast computing photovoltaic model for string simulation under partial shading condition[J]. Sustainable Energy Technologies and Assessments, 2020, 42: 100812.
[3] H LIU, GAO Q, MA P. Photovoltaic generation power prediction research based on high quality contextontology and gated recurrent neural network[J]. Sustainable Energy Technologies and Assessments, 2021, 45(16): 101191.
[4] PRASAD S. Remotely sensed data characterization, classification, and accuracies[M]. Boca Raton: CRC Press, 2015: 7.
[5] KAPLAN G, AVDAN U. Object-based water body extraction model using sentinel-2 satellite imagery[J]. European Journal of Remote Sensing, 2017, 50(1): 137-143.
[6] JING L, HU B, NOLAND T, et al. An individual tree crown delineation method based on multi-scale segmentation of imagery[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2012, 70: 88-98.
[7] MOU L, ZHU X X. Vehicle instance segmentation from aerial image and video using a multitask learningresidual fully convolutional network[J]. IEEE Transactions on Geoscience and Remote Sensing, 2018, 56(11): 6699-6711.
[8] JAKUBOWSKI M K, LI W, GUO Q, et al. Delineating individual trees from LiDAR data: a comparison of vector-and raster-based segmentation approaches[J]. Remote Sensing, 2013, 5(9): 4163-4186.
[9] KOTARIDIS I, LAZARIDOU M. Remote sensing image segmentation advances: a meta-analysis[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2021, 173: 309-322.
[10] JIE Y, JI X, YUE A, et al. Combined multi-layer feature fusion and edge detection method for distributed photovoltaic power station identification[J]. Energies, 2020, 13(24): 6742.
[11] 吴永静, 吴锦超, 林超, 等. 基于深度学习的高分辨率遥感影像光伏用地提取[J]. 测绘通报, 2021(5): 96-101.
WU Y J, WU J C, LIN C, et al. Photovoltaic land extraction from high-resolution remote sensing images based on deep learning method[J]. Bulletin of Surveying and Mapping, 2021(5): 96-101.
[12] ZHANG H, WU C, ZHANG Z, et al. ResNeSt: split-attention networks[C]//Proceedings of the 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022: 2736-2746.
[13] CHEN L C, ZHU Y, PAPANDREOU G, et al. Encoder-decoder with atrous separable convolution for semantic image segmentation[C]//Proceedings of the 15th European Conference on Computer Vision, 2018: 801-818.
[14] LONG J, SHELHAMER E, DARRELL T. Fully convolutional networks for semantic segmentation[C]//Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition, 2015: 3431-3440.
[15] RONNEBERGER O, FISCHER P, BROX T. U-net: convolutional networks for biomedical image segmentation[C]//Proceedings of the 18th International Conference on Medical Image Computing and Computer-Assisted Intervention. Cham: Springer, 2015: 234-241.
[16] BADRINARAYANAN V, KENDALL A, CIPOLLA R. SegNet: a deep convolutional encoder-decoder architecture for image segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(12): 2481-2495.
[17] ZHAO H, SHI J, QI X, et al. Pyramid scene parsing network[C]//Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, 2017: 2881-2890.
[18] CHEN L C, PAPANDREOU G, KOKKINOS I, et al. DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 40(4): 834-848.
[19] CHAURASIA A, CULURCIELLO E. LinkNet: exploiting encoder representations for efficient semantic segmentation[C]//Proceedings of the 2017 IEEE Visual Communications and Image Processing, 2017: 1-4.
[20] ZHOU L, ZHANG C, MING W. D-LinkNet: LinkNet with pretrained encoder and dilated convolution for high resolution satellite imagery road extraction[C]//Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2018.
[21] ZHENG X, CHEN T. Segmentation of high spatial resolution remote sensing image based on U-Net convolutional networks[C]//Proceedings of the 2020 IEEE International Geoscience and Remote Sensing Symposium, 2020: 2571-2574.
[22] LI P, ZHANG H, GUO Z, et al. Understanding rooftop PV panel semantic segmentation of satellite and aerial images for better using machine learning[J]. Advances in Applied Energy, 2021, 4: 100057.
[23] CASTELLO R, ROQUETTE S, ESGUERRA M, et al. Deep learning in the built environment: automatic detection of rooftop solar panels using convolutional neural networks[J]. Journal of Physics: Conference Series, 2019, 1343(1): 012034.
[24] ZHUANG L, ZHANG Z, WANG L. The automatic segmentation of residential solar panels based on satellite images: a cross learning driven U-Net method[J]. Applied Soft Computing, 2020, 92: 106283.
[25] JIE Y, JI X, YUE A, et al. Combined multi-layer feature fusion and edge detection method for distributed photovoltaic power station identification[J]. Energies, 2020, 13(24): 6742.
[26] CHEN L C, PAPANDREOU G, SCHROFF F, et al. Rethinking atrous convolution for semantic image segmentation[J]. arXiv:1706.05587, 2017.
[27] YU F, KOLTUN V. Multi-scale context aggregation by dilated convolutions[J]. arXiv:1511.07122, 2015.
[28] VASWANI A, SHAZEER N, PARMAR N, et al. Attention is all you need[C]//Advances in Neural Information Processing Systems 30, 2017.
[29] JADERBERG M, SIMONYAN K, ZISSERMAN A. Spatial transformer networks[C]//Advances in Neural Information Processing Systems 28, 2015.
[30] HU J, SHEN L, SUN G. Squeeze-and-excitation networks[C]//Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition, 2018: 7132-7141.
[31] YU C, WANG J, PENG C, et al. Learning a discriminative feature network for semantic segmentation[C]//Proceedings of the 2018 IEEE Conference on Computer Vison and Pattern Recognition, 2018: 1857-1866.
[32] LI H, QIU K, CHEN L, et al. SCAttNet: Semantic segmentation network with spatial and channel attention mechanism for high-resolution remote sensing images[J]. IEEE Geoscience and Remote Sensing Letters, 2020, 18(5): 905-909.
[33] WU M, ZHANG C, LIU J, et al. Towards accurate high resolution satellite image semantic segmentation[J]. IEEE Access, 2019, 7: 55609-55619.
[34] ZI W, XIONG W, CHEN H, et al. SGA-Net: self-constructing graph attention neural network for semantic segmentation of remote sensing images[J]. Remote Sensing, 2021, 13(21): 4201.
[35] FU J, LIU J, TIAN H, et al. Dual attention network for scene segmentation[C]//Proceedings of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019: 3146-3154.
[36] PENG C, ZHANG X, YU G, et al. Large kernel matters-improve semantic segmentation by global convolutional network[C]//Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition, 2017: 4353-4361.
[37] LU W, LIANG L, WU X, et al. An adaptive multiscale fusion network based on regional attention for remote sensing images[J]. IEEE Access, 2020, 8: 107802-107813.
[38] SZEGEDY C, VANHOUCKE V, IOFFE S, et al. Re-thinking the inception architecture for computer vision[C]//Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, 2016: 2818-2826.
[39] MILLETARI F, NAVAB N, AHMADI S A. V-net: fully convolutional neural networks for volumetric medical image segmentation[C]//Proceedings of the 2016 4th International Conference on 3D Vision, 2016: 565-571.
[40] JIANG H, YAO L, LU N, et al. Multi-resolution dataset for photovoltaic panel segmentation from satellite and aerial imagery[J]. Earth System Science Data, 2021, 13(11): 5389-5401.