[4] [4] Krizhevsky, Alex, Hinton, et al. ImageNet Classification with Deep Convolutional Neural Networks[J]. Commun ACM, 2017, 60(6): 84-90.

[5] [5] E. Shelhamer, J. Long and T. Darrell, Fully Convolutional Networks for Semantic Segmentation,[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(4): 640-651.

[6] [6] Noh H, Hong S, Han B. Learning deconvolution network for semantic segmentation[C]//Proceedings of the IEEE international conference on computer vision. 2015: 1520-1528.

[7] [7] 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.

[8] [8] Ronneberger O, Fischer P, Brox T. U-net: Convolutional networks for biomedical image segmentation[C]//Medical Image Computing and Computer-Assisted Intervention-MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18. Springer International Publishing, 2015: 234-241.

[9] [9] Milletari F, Navab N, Ahmadi S A. V-net: Fully convolutional neural networks for volumetric medical image segmentation[C]//2016 fourth international conference on 3D vision (3DV). IEEE, 2016: 565-571.

[10] [10] Chen L C, Papandreou G, Kokkinos I, et al. Semantic image segmentation with deep convolutional nets and fully connected crfs[J]. arXiv preprint arXiv: 1412.7062, 2014.

[11] [11] 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.

[12] [12] Chen L C, Zhu Y, Papandreou G, et al. Encoder-decoder with atrous separable convolution for semantic image segmentation[C]//Proceedings of the European conference on computer vision (ECCV). 2018: 801-818.

[13] [13] Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need[J]. Advances in Neural Information Processing Systems, 2017, 30: 1-10.

[14] [14] Dosovitskiy A, Beyer L, Kolesnikov A, et al. An image is worth 16×16 words: Transformers for image recognition at scale[J]. arXiv preprint arXiv: 2010.11929, 2020.

[15] [15] Zheng S, Lu J, Zhao H, et al. Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2021: 6881-6890.

[16] [16] Xie E, Wang W, Yu Z, et al. SegFormer: Simple and efficient design for semantic segmentation with transformers[J]. Advances in Neural Information Processing Systems, 2021, 34: 12077-12090.

[18] [18] Wang Q, Wu B, Zhu P, et al. ECA-Net: Efficient channel attention for deep convolutional neural networks[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2020: 11534-11542.

[19] [19] Hu J, Shen L, Sun G. Squeeze-and-excitation networks[C]//2018 IEEE conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, June 18-22, 2018. NewYork: IEEE Press, 2018: 7132-7141

[20] [20] Liu H, Liu F, Fan X, et al. Polarized self-attention: Towards high-quality pixel-wise mapping[J]. Neurocomputing, 2022, 506: 158-167.

[21] [21] Tong X Y, Xia G S, Lu Q, et al. Land-cover classification with high-resolution remote sensing images using transferable deep models[J]. Remote Sensing of Environment, 2020, 237: 111322.

[22] [22] Ji S, Wei S, Lu M. Fully convolutional networks for multisource building extraction from an open aerial and satellite imagery data set[J]. IEEE Transactions on geoscience and remote sensing, 2018, 57(1): 574-586.