[1] SUN Qiao, WU Jiawei. The role of high-precision mapping in urban planning[J]. The Oretical Research in Urban Construction, 175-177(2024).

[2] RAJABI R, ZEHTABIAN A, SINGH K D et al. Hyperspectral imaging in environmental monitoring and analysis[J]. Frontiers in Environmental Science, 19, 885-894(2023).

[3] HEARST M A, DUMAIS S T, OSUNA E et al. Support vector machines[J]. IEEE Intelligent Systems and Their Applications, 13, 18-28(1998).

[4] LIU Xiushan, PENG Weifeng, CAI Jun. Research on road extraction from high-resolution optical remote sensing image based on support vector machine[J]. Laser Journal, 42, 72-75(2021).

[5] BREIMAN L. Random forests[J]. Machine Learning, 45, 5-32(2001).

[6] LIANG Jinchao, CHEN Chao, ZHANG Zili et al. A random forest-based method integrating indices and principal components for classifying remote sensing images[J]. Remote Sensing for Nature Resources, 35, 35-42(2023).

[7] LONG J, SHELHAMER E, DARRELL T. Fully convolutional networks for semantic segmentation[C], 3431-3440(2015).

[8] SHEN Yanshan, WANG Achuan. Remote sensing image feature segmentation method based on deep learning[J]. Chinese Journal of Liquid Crystals and Displays, 36, 733-740(2021).

[9] 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).

[10] GUAN Shijie, LIU Jihao, JIANG Yueqiu. Research on semantic segmentation algorithm of remote sensing images basedon improved SegNet[J]. Journal of Ordnance Equipment Engineering, 44, 237-242(2023).

[11] RONNEBERGER O, FISCHER P, BROX T. U-net: convolutional networks for biomedical image segmentation[C], 234-241(2015).

[12] CHENG B, MISRA I, SCHWING A G et al. Masked-attention mask transformer for universal image segmentation[C], 1290-1299(2022).

[13] ZHAO H, SHI J, QI X et al. Pyramid scene parsing network[C], 2881-2890(2017).

[14] HU J, SHEN L, SUN G. Squeeze-and-excitation networks[C], 7132-7141(2018).

[15] YUAN Y, XIE J, CHEN X et al. Segfix: model-agnostic boundary refinement for segmentation[C], 489-506(2020).

[16] 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, 237, 111322(2020).

[17] VASWANI A, SHAZEER N, PARMAR N et al. Attention is all you need[J]. Advances in Neural Information Processing Systems, 30, 13756489(2017).

[18] ABDULLA W. Mask R-CNN for object detection and instance segmentation on Keras and TensorFlow[EB/OL]. https://github.com/matterport/Mask_RCNN

[19] LIU M, YIN H. Cross attention network for semantic segmentation[C], 2434-2438(2019).

[20] 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?file=1412.7062

[21] LIN M, CHEN Q, YAN S. Network in network[EB/OL]. https://arxiv.org/abs/1312.4400

[22] NAIR V, HINTON G E. Rectified linear units improve restricted boltzmann machines[C], 807-814(2010).

[23] IOFFE S, SZEGEDY C. Batch normalization: accelerating deep network training by reducing internal covariate shift[C], 448-456(2015).

[24] 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(2017).

[25] REZATOFIGHI H S, TSOI N, GWAK J et al. Generalized intersection over union: a metric and a loss for bounding box regression[C], 658-666(2019).

[26] ZHANG Zhehan, FANG Wei, DU Lili et al. Semantic segmentation of remote sensing image based on encoder-decoder convolutional neural network[J]. Acta Optica Sinica, 40, 46-55(2020).

[27] ZHAO J, ZHANG D, SHI B et al. Multi-source collaborative enhanced for remote sensing images semantic segmentation[J]. Neurocomputing, 493, 76-90(2022).