The agricultural greenhouse is a kind of agricultural facility, which is divided into transparent and non-transparent according to the surface transmittance. The large-scale statistics of agricultural greenhouses are of great significance to the survey of agricultural facilities, the formulation of agricultural policies, and the planning of county economic development. Aiming at the problem that manual statistics are time-consuming and laborious, this paper utilizes the convolutional neural network to extract agricultural greenhouses information from high-resolution remote sensing images. To solve the problems of insufficient semantic information extraction in remote sensing images and insufficient utilization of edge information of the U-Net model, this paper proposes the following improvements: 1) The semantic segmentation task is optimized, and ConvNeXt and attention mechanism is utilized to extract deep semantic information of agricultural greenhouses in remote sensing images. 2) The edge detection task is introduced, and the gated convolution layer and concate operation are used to fuse the semantic features of the encoder and the image gradient output by the decoder, and then the edge information is combined to optimize the segmentation results. After testing, the improved model can extract both transparent and non-transparent agricultural greenhouses information at the same time and the recognition effect is good, which is greatly improved compared with the traditional method.
[1] 陈经纬, 李宇, 陈俊, 等. 基于MFF-Deeplabv3+ 网络的高分辨率遥感影像建筑物提取方法[J/OL]. 中国科学院大学学报.(2023-03-21)[2023-05-10]. DOI:10.7523/j.ucas.2023.010.
[2] 王雪英, 郭卫华. 面向对象的高分一号卫星影像大棚信息提取研究[J].湖北农业科学, 2019,58(24):217-220.DOI:10.14088/j.cnki.issn0439-8114.2019.24.053.
[3] 汤紫霞, 李蒙蒙, 汪小钦, 等. 基于GF-2遥感影像的葡萄大棚信息提取[J].中国农业科技导报, 2020,22(11):95-105.DOI:10.13304/j.nykjdb.2019.0759.
[4] Yang D D, Chen J, Zhou Y, et al. Mapping plastic greenhouse with medium spatial resolution satellite data: development of a new spectral index[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2017, 128: 47-60.DOI:10.1016/j.isprsjprs.2017.03.002.
[5] Shi L F, Huang X J, Zhong T Y, et al. Mapping plastic greenhouses using spectral metrics derived from GaoFen-2 satellite data[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2019, 13: 49-59.DOI:10.1109/JSTARS.2019.2950466.
[6] Ronneberger O, Fischer P, Brox T. U-net: convolutional networks for biomedical image segmentation[C]//International Conference on Medical Image Computing and Computer-Assisted Intervention. Cham: Springer, 2015: 234-241.DOI:10.1007/978-3-319-24574-4_28.
[7] Sun K, Xiao B, Liu D, et al. Deep high-resolution representation learning for human pose estimation[C]//2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). June 15-20, 2019, Long Beach, CA, USA.IEEE,2020:5686-5696.DOI: 10.1109/CVPR.2019.00584.
[8] Tan M X, Le Q V. EfficientNet: rethinking model scaling for convolutional neural networks[EB/OL]. 2019: arXiv: 1905.11946.(2019-05-24)[2023-05-10]. https://arxiv.org/abs/1905.11946.
[9] Li M, Zhang Z J, Lei L P, et al. Agricultural greenhouses detection in high-resolution satellite images based on convolu-tional neural networks: comparison of faster R-CNN, YOLO v3 and SSD[J]. Sensors, 2020, 20(17): 4938. DOI: 10.3390/s20174938.
[10] Redmon J, Farhadi A. YOLOv3: An incremental improve-ment[EB/OL]. 2018: arXiv: 1804.02767.(2018-04-08)[2023-05-10]. https://arxiv.org/abs/1804.02767.
[11] Feng Q L, Niu B W, Chen B A, et al. Mapping of plastic greenhouses and mulching films from very high resolution remote sensing imagery based on a dilated and non-local convolutional neural network[J]. International Journal of Applied Earth Observation and Geoinformation, 2021, 102: 102441. DOI: 10.1016/j.jag.2021.102441.
[12] Baghirli O, Ibrahimli I, Mammadzada T. Greenhouse seg-mentation on high-resolution optical satellite imagery using deep learning techniques[EB/OL]. 2020: arXiv: 2007. 11222.(2020-07-22)[2023-05-10]. https://arxiv.org/abs/2007.11222.
[13] Feng J N, Wang D L, Yang F, et al. PODD: a dual-task detection for greenhouse extraction based on deep learning[J]. Remote Sensing, 2022, 14(19): 5064. DOI: 10. 3390/rs14195064.
[14] Zhang X P, Cheng B, Chen J F, et al. High-resolution boundary refined convolutional neural network for automatic agricultural greenhouses extraction from GaoFen-2 satellite imageries[J]. Remote Sensing, 2021, 13(21): 4237. DOI: 10.3390/rs13214237.
[15] Zhang Z X, Liu Q J, Wang Y H. Road extraction by deep residual U-net[J]. IEEE Geoscience and Remote Sensing Letters, 2018, 15(5): 749-753. DOI: 10.1109/LGRS.2018.2802944.
[16] Liu Z, Mao H Z, Wu C Y, et al. A ConvNet for the 2020s[C]//2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). June 18-24, 2022, New Orleans, LA, USA. IEEE, 2022: 11966-11976. DOI: 10.1109/CVPR 52688.2022.01167.
[17] He K M, Zhang X Y, Ren S Q, et al. Deep residual learning for image recognition[C]//2016 IEEE Conference on Com-puter Vision and Pattern Recognition (CVPR). June 27-30, 2016, Las Vegas, NV, USA. IEEE, 2016: 770-778. DOI: 10.1109/CVPR.2016.90.
[18] Liu Z, Lin Y T, Cao Y, et al. Swin transformer: hierarchical vision transformer using shifted windows[C]//2021 IEEE/CVF International Conference on Computer Vision (ICCV). October 10-17, 2021, Montreal, QC, Canada. IEEE, 2022: 9992-10002. DOI: 10.1109/ICCV48922.2021.00986.
[19] Hou Q B, Zhou D Q, Feng J S. Coordinate attention for efficient mobile network design[C]//2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). June 20-25, 2021, Nashville, TN, USA. IEEE, 2021: 13708-13717. DOI: 10.1109/CVPR46437.2021. 01350.
[20] Woo S, Park J, Lee J Y, et al. CBAM: convolutional block attention module[C]//European Conference on Computer Vision. Cham: Springer, 2018: 3-19.DOI:10.1007/978-3-030-01234-2_1.
[21] Takikawa T, Acuna D, Jampani V, et al. Gated-SCNN: gated shape CNNs for semantic segmentation[C]//2019 IEEE/CVF International Conference on Computer Vision (ICCV). October 27-November 2, 2019, Seoul, Korea (South). IEEE, 2020: 5228-5237. DOI: 10.1109/ICCV.2019.00533.
[22] Zhen M M, Wang J L, Zhou L, et al. Joint semantic segmentation and boundary detection using iterative pyramid contexts[C]//2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). June 13-19, 2020, Seattle, WA, USA. IEEE, 2020: 13663-13672. DOI: 10.1109/CVPR42600.2020.01368.
[23] Loshchilov I, Hutter F. Fixing weight decay regularization in Adam[EB/OL]. 2017: arXiv: 1711.05101.(2017-11-14)[2023-05-10]. https://arxiv.org/abs/1711.05101.
[24] Ding L, Tang H, Bruzzone L. LANet: local attention embedding to improve the semantic segmentation of remote sensing images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 59(1): 426-435. DOI: 10.1109/TGRS.2020.2994150.
[25] Chen L C, Zhu Y K, Papandreou G, et al. Encoder-decoder with atrous separable convolution for semantic image segmen-tation[C]//European Conference on Computer Vision. Cham: Springer, 2018: 833-851.10.1007/978-3-030-01234-2_49.
