[1] Hu J, Zhang Y, Xie S Y. Summary of research progress on application of domestic remote sensing image classification technology[J]. Computer Engineering and Applications, 57, 1-13(2021).

[2] Zhang Q K, Meng J H, Ren C. Crop classification based on two-dimensional representation and CNN model from remote sensing[J]. National Remote Sensing Bulletin, 26, 1437-1449(2022).

[3] Shelhamer E, Long J, Darrell T. Fully convolutional networks for semantic segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 640-651(2015).

[4] Xie J Y, Ding L X, Wang Z H et al. Classification of coastal wetland vegetation utilizing FCN and object-oriented methods[J]. Scientia Silvae Sinicae, 56, 98-106(2020).

[5] Ronneberger O, Fischer P, Brox T. U-net: convolutional networks for biomedical image segmentation[M]. Navab N, Hornegger J, Wells W M, et al. Medical image computing and computer-assisted intervention-MICCAI 2015. Lecture notes in computer science, 9351, 234-241(2015).

[6] Li C L, Huang F H, Hu W et al. Building extraction from high-resolution remote sensing image based on Res-AttentionUnet[J]. Journal of Geo-Information Science, 23, 2232-2243(2021).

[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, 39, 2481-2495(2017).

[8] Zhang Z H, Fang W, Du L L et al. Semantic segmentation of remote sensing image based on encoder-decoder convolutional neural network[J]. Acta Optica Sinica, 40, 0310001(2020).

[9] Chen L C, Papandreou G, Kokkinos I et al. Semantic image segmentation with deep convolutional nets and fully connected CRFs[EB/OL]. https://arxiv.org/abs/1412.7062

[10] 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, 40, 834-848(2018).

[11] Chen L C, Papandreou G, Schroff F et al. Rethinking atrous convolution for semantic image segmentation[EB/OL]. https://arxiv.org/abs/1706.05587

[12] Chen L C, Zhu Y K, Papandreou G et al. Encoder-decoder with atrous separable convolution for semantic image segmentation[M]. Ferrari V, Hebert M, Sminchisescu C, et al. Computer vision-ECCV 2018. Lecture notes in computer science, 11211, 833-851(2018).

[13] Xu Z Y, Shen Z F, Li Y et al. Classification of high-resolution remote sensing images based on enhanced DeepLab algorithm and adaptive loss function[J]. National Remote Sensing Bulletin, 26, 406-415(2022).

[14] Chollet F. Xception: deep learning with depthwise separable convolutions[C], 1800-1807(2017).

[15] Sandler M, Howard A, Zhu M L et al. MobileNetV2: inverted residuals and linear bottlenecks[C], 4510-4520(2018).

[16] Zhou M T, Sui H G, Chen S X et al. BT-RoadNet: a boundary and topologically-aware neural network for road extraction from high-resolution remote sensing imagery[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 168, 288-306(2020).

[17] Huang J F, Zhang X C, Sun Y et al. Attention-guided label refinement network for semantic segmentation of very high resolution aerial orthoimages[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, 4490-4503(2021).

[18] Zhu R, Ye K, Yang B et al. Feature classification method based on improved DeeplabV3+[J]. Computer Science, 48, 382-385(2021).

[19] Xu Z F, Du H B, Han C L et al. Improved based on DeepLab V3+ network[J]. Intelligent Computer and Applications, 11, 181-184(2021).