[1] [1] Nogueira K, Mura M D, Chanussot J, et al. Dynamic multicontext segmentation of remote sensing images based on convolutional networks[J]. IEEE Transactions on Geoscience & Remote Sensing, 2019, 57(10): 7503-7520.

[2] [2] Keiller N, Fadel S G, Dourado I C, et al. Exploiting convnet diversity for flooding identification[J]. IEEE Geoscience & Remote Sensing Letters, 2017, 15(9): 1446-1450.

[3] [3] Zhang Q L, Seto K C. Mapping urbanization dynamics at regional and global scales using multi-temporal DMSP/OLS nighttime light data[J]. Remote Sensing of Environment, 2011, 115(9): 2320-2329.

[4] [4] Zhao Q, Liu J H, Li Y W, et al. Semantic segmentation with attention mechanism for remote sensing images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 60(3): 1-13.

[6] [6] Chen J, Wang H, Guo Y, et al. Strengthen the feature distinguishability of geo-object details in the semantic segmentation of high-resolution remote sensing images[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021, 14: 2327-2340.

[7] [7] Nong Z X, Su X, Liu Y, et al. Boundary-aware dual-stream network for VHR remote sensing images semantic segmentation[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021, 14: 5260-5268.

[8] [8] Fauvel M, Tarabalka Y, Benediktsson J A, et al. Advances in spectral-spatial classification of hyperspectral images[J]. Proceedings of the IEEE, 2013, 101(3): 652-675.

[9] [9] Gueguen L. Classifying compound structures in satellite Images: A compressed representation for fast queries[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 53 (4): 1803-1818.

[10] [10] Volpi M, Tuia D. Dense semantic labeling of subdecimeter resolution images with convolutional neural networks[J]. IEEE Transactions on Geoscience & Remote Sensing, 2016, 55(2): 881-893.

[11] [11] Cun Y L, Boser B, Denker J S, et al. Handwritten digit recognition with a back-propagation network[C]//Advances in Neural Information Processing Systems, 1990: 396-404.

[12] [12] Hang R L, Li Z, Ghamisi P, et al. Classification of hyperspectral and LiDAR data using coupled CNNs[J]. IEEE Transactions on Geoscience and Remote Sensing, 2020, 58 (7): 4939-4950.

[13] [13] Liu W J, Zhang W K, Sun X, et al. HECR-Net: Heightembedding context reassembly network for semantic segmentation in aerial images[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021, 14: 9117-9131.

[14] [14] Zhou F, Hang R L, Liu Q S, et al. Pyramid fully convolutional network for hyperspectral and multispectral image fusion[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2019, 12(5): 1549-1558.

[16] [16] Long J, Shelhamer E, Darrell T. Fully convolutional networks for semantic segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 39(4): 640 -651.

[17] [17] Badrinarayanan V, Kendall A, Cipolla R. SegNet: A deep convolutional encoder-decoder architecture for image segmentation[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2017, 39(12): 2481-2495.

[18] [18] Maggiori E, Tarabalka Y, Charpiat G, et al. High-resolution aerial image labeling with convolutional neural networks[J]. IEEE Transactions on Geoscience and Remote Sensing, 2017, 55(12): 7092-7103.

[19] [19] Pan X, Zhao J, Xu J. Conditional generative adversarial network-based training sample set improvement model for the semantic segmentation of high-resolution remote sensing images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 59(9): 7854-7870.

[20] [20] Li M L, Shan L, Li X B, et al. Global-local attention network for semantic segmentation in aerial images[C]//International Conference on Pattern Recognition, 2021: 5704-5711.

[21] [21] Liu R, Mi L, Chen Z. AFNet: Adaptive fusion network for remote sensing image semantic segmentation[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 59 (9): 7871-7886.

[22] [22] Zheng Z, Zhong Y F, Wang J, et al. Foreground-aware relation network for geospatial object segmentation in high spatial resolution remote sensing imagery[C]//IEEE Conference on Computer Vision and Pattern Recognition, 2020: 4095-4104.

[23] [23] Wang L B, Li R, Duan C X, et al. A novel transformer based semantic segmentation scheme for fine-resolution remote sensing images[J]. IEEE Geoscience and Remote Sensing Letters, 2022, 19: 1-5.

[25] [25] Zhao H S, Shi J P, Qi X J, et al. Pyramid scene parsing network[C]//IEEE Conference on Computer Vision and Pattern Recognition, 2017: 6230-6239.

[26] [26] Liu W, Rabinovich A, Berg A C. ParseNet: Looking wider to see better[EB]. arXiv: 1506.04579, 2015.

[27] [27] Chen L C, Papandreou G, Kokkinos I, et al. DeepLab: Semantic image segmentation with deep convolution-al nets, Atrous convolution, and fully connected C-RFs[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018, 40(4): 834-848.

[28] [28] 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, 2015: 234-241.

[29] [29] Chen L C, Zhu Y K, Papandreou G, et al. Encoder-decoder with Atrous separable convolution for semantic image segmentation[C]//European Conference on Computer Vision, 2018: 801-818.

[30] [30] Du S J, Du S H, Liu B, et al. Incorporating DeepLabv-3+ and object-based image analysis for semantic segmentation of very high resolution remote sensing images[J]. International Journal of Digital Earth, 2021, 14(3): 357-378.

[31] [31] Sun K, Xiao B, Liu D, et al. Deep high-resolution re-presentation learning for human pose estimation[C]//IEEE Conference on Computer Vision and Pattern Recognition, 2019: 5693-5703.

[32] [32] He K M, Zhang X Y, Ren S Q, et al. Deep residual learning for image recognition[C]//IEEE Conference on Computer Vision and Pattern Recognition, 2016: 770-778.

[33] [33] Hou Q B, Zhang L, Cheng M, et al. Strip pooling: Rethinking spatial pooling for scene parsing[C]//IEEE/CVF Conference on Computer Vision and Pattern Re-cognition, 2020: 4003-4012.

[34] [34] Diakogiannis F I, Waldner F, Caccetta P, et al. ResU-Neta: A deep learning framework for semantic seg-mentation of remotely sensed data[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2020, 162: 94-114.

[35] [35] Zhang H, Dana K, Shi J P, et al. Context encoding for semantic segmentation[C]//IEEE Conference on Computer Vision and Pattern Recognition, 2018: 7151-7160.

[36] [36] Fu J, Liu J, Tian H J, et al. Dual attention network for scene segmentation[C]//IEEE Conference on Computer Vision and Pattern Recognition, 2019: 3141-3149.

[37] [37] Zheng X W, Huan L X, Xia G S, et al. Parsing very high resolution urban scene images by learning deep ConvNets with edge-aware loss[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2020, 170: 15-28.