A review of remote sensing image segmentation by deep learning methods

References

• Abbas, Amreen, Sweta Jain, Mahesh Gour, and Swetha Vankudothu. 2021. “Tomato Plant Disease Detection Using Transfer Learning with C-GAN Synthetic Images.” Computers and Electronics in Agriculture 187:106279. https://doi.org/10.1016/j.compag.2021.106279.
   Web of Science ®Google Scholar
• Aleissaee, Abdulaziz Amer, Amandeep Kumar, Rao Muhammad Anwer, Salman Khan, Hisham Cholakkal, Gui-Song Xia, and Fahad Shahbaz Khan. 2023. “Transformers in Remote Sensing: A Survey.” Remote Sensing 15 (7): 1860. https://doi.org/10.3390/rs15071860.
   Web of Science ®Google Scholar
• Ali, Muhammad Ichsan, Gufran Darma Dirawan, Abdul Hafid Hasim, and Muh Rais Abidin. 2019. “Detection of Changes in Surface Water Bodies Urban Area with NDWI and MNDWI Methods.” International Journal on Advanced Science Engineering Information Technology 9 (3): 946–951. https://doi.org/10.18517/ijaseit.9.3.8692.
   Google Scholar
• Badrinarayanan, Vijay, Alex Kendall, and Roberto Cipolla. 2017. “Segnet: A Deep Convolutional Encoder–Decoder Architecture for Image Segmentation.” IEEE Transactions on Pattern Analysis and Machine Intelligence 39 (12): 2481–2495. https://doi.org/10.1109/TPAMI.34.
   PubMed Web of Science ®Google Scholar
• Bai, Lubin, Shihong Du, Xiuyuan Zhang, Haoyu Wang, Bo Liu, and Song Ouyang. 2022. “Domain Adaptation for Remote Sensing Image Semantic Segmentation: An Integrated Approach of Contrastive Learning and Adversarial Learning.” IEEE Transactions on Geoscience and Remote Sensing 60:1–13.
   Web of Science ®Google Scholar
• Bandara, Wele Gedara Chaminda, Nithin Gopalakrishnan Nair, and Vishal M. Patel. 2022. “DDPM-CD: Remote Sensing Change Detection using Denoising Diffusion Probabilistic Models.” arXiv preprint arXiv: 2206.11892.
   Google Scholar
• Baranchuk, Dmitry, Ivan Rubachev, Andrey Voynov, Valentin Khrulkov, and Artem Babenko. 2021. “Label-Efficient Semantic Segmentation with Diffusion Models.” arXiv preprint arXiv: 2112.03126.
   Google Scholar
• Basaeed, Essa, Harish Bhaskar, and Mohammed Al-Mualla. 2016. “Supervised Remote Sensing Image Segmentation Using Boosted Convolutional Neural Networks.” Knowledge-Based Systems 99:19–27. https://doi.org/10.1016/j.knosys.2016.01.028.
   Web of Science ®Google Scholar
• Bijeesh, T. V., and K. N. Narasimhamurthy. 2020. “Surface Water Detection and Delineation Using Remote Sensing Images: A Review of Methods and Algorithms.” Sustainable Water Resources Management 6 (1): 1–23. https://doi.org/10.1007/s40899-020-00425-4.
   Google Scholar
• Bolanos, Sandra, Doug Stiff, Brian Brisco, and Alain Pietroniro. 2016. “Operational Surface Water Detection and Monitoring Using Radarsat 2.” Remote Sensing 8 (4): 285. https://doi.org/10.3390/rs8040285.
   Web of Science ®Google Scholar
• Brunekreef, Bert, and Stephen T. Holgate. 2002. “Air Pollution and Health.” The Lancet 360 (9341): 1233–1242. https://doi.org/10.1016/S0140-6736(02)11274-8.
   PubMed Web of Science ®Google Scholar
• Caron, Mathilde, Ishan Misra, Julien Mairal, Priya Goyal, Piotr Bojanowski, and Armand Joulin. 2020. “Unsupervised Learning of Visual Features by Contrasting Cluster Assignments.” Advances in Neural Information Processing Systems 33:9912–9924.
   Google Scholar
• Caron, Mathilde, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, and Armand Joulin. 2021. “Emerging Properties in Self-Supervised Vision Transformers.” In Proceedings of the IEEE/CVF International Conference on Computer Vision, 9650–9660.
   Google Scholar
• Castelluccio, Marco, Giovanni Poggi, Carlo Sansone, and Luisa Verdoliva. 2015. “Land Use Classification in Remote Sensing Images by Convolutional Neural Networks.” arXiv preprint arXiv: 1508.00092.
   Google Scholar
• Chawla, Ila, L Karthikeyan, and Ashok K. Mishra. 2020. “A Review of Remote Sensing Applications for Water Security: Quantity, Quality, and Extremes.” Journal of Hydrology 585:124826. https://doi.org/10.1016/j.jhydrol.2020.124826.
   Web of Science ®Google Scholar
• Chen, Ting, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. 2020. “A Simple Framework for Contrastive Learning of Visual Representations.” In International Conference on Machine Learning, 1597–1607.
   Google Scholar
• Chen, Ting, Simon Kornblith, Kevin Swersky, Mohammad Norouzi, and Geoffrey E. Hinton. 2020. “Big Self-Supervised Models are Strong Semi-Supervised Learners.” Advances in Neural Information Processing Systems 33:22243–22255.
   Google Scholar
• Chen, Yushi, Zhouhan Lin, Xing Zhao, Gang Wang, and Yanfeng Gu. 2014. “Deep Learning-Based Classification of Hyperspectral Data.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 7 (6): 2094–2107. https://doi.org/10.1109/JSTARS.4609443.
   Web of Science ®Google Scholar
• Chen, Yanbei, Massimiliano Mancini, Xiatian Zhu, and Zeynep Akata. 2024. “Semi-Supervised and Unsupervised Deep Visual Learning: A Survey.” IEEE Transactions on Pattern Analysis and Machine Intelligence 46 (3): 1327–1347.
   PubMedGoogle Scholar
• Chen, Liang-Chieh, George Papandreou, Iasonas Kokkinos, Kevin Murphy, and Alan L. Yuille. 2014. “Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs.” arXiv preprint arXiv: 1412.7062.
   Google Scholar
• Chen, Liang-Chieh, George Papandreou, Iasonas Kokkinos, Kevin Murphy, and Alan L. Yuille. 2017. “Deeplab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs.” IEEE Transactions on Pattern Analysis and Machine Intelligence 40 (4): 834–848. https://doi.org/10.1109/TPAMI.2017.2699184.
   PubMed Web of Science ®Google Scholar
• Chen, Liang-Chieh, George Papandreou, Florian Schroff, and Hartwig Adam. 2017. “Rethinking Atrous Convolution for Semantic Image Segmentation.” arXiv preprint arXiv: 1706.05587.
   Google Scholar
• Chen, Ning, Jun Yue, Leyuan Fang, and Shaobo Xia. 2023. “SpectralDiff: A Generative Framework for Hyperspectral Image Classification With Diffusion Models.” IEEE Transactions on Geoscience and Remote Sensing 61:1–16.
   Google Scholar
• Chen, Liang-Chieh, Yukun Zhu, George Papandreou, Florian Schroff, and Hartwig Adam. 2018. “Encoder–Decoder with Atrous Separable Convolution for Semantic Image Segmentation.” In Proceedings of the European Conference on Computer Vision (ECCV), 801–818.
   Google Scholar
• Crivelari-Costa, Patrícia Monique, Mendelson Lima, Newton La Scala Jr, Fernando Saragosa Rossi, João Lucas Della-Silva, Ricardo Dalagnol, Paulo Eduardo Teodoro, et al. 2023. “Changes in Carbon Dioxide Balance Associated with Land Use and Land Cover in Brazilian Legal Amazon Based on Remotely Sensed Imagery.” Remote Sensing 15 (11): 2780.
   Web of Science ®Google Scholar
• Cui, Binge, Xin Chen, and Yan Lu. 2020. “Semantic Segmentation of Remote Sensing Images Using Transfer Learning and Deep Convolutional Neural Network with Dense Connection.” IEEE Access 8: 116744–116755. https://doi.org/10.1109/Access.6287639.
   Web of Science ®Google Scholar
• Diakogiannis, Foivos I., François Waldner, Peter Caccetta, and Chen Wu. 2020. “ResUNet-a: A Deep Learning Framework for Semantic Segmentation of Remotely Sensed Data.” ISPRS Journal of Photogrammetry and Remote Sensing 162:94–114. https://doi.org/10.1016/j.isprsjprs.2020.01.013.
   Web of Science ®Google Scholar
• Digra, Monia, Renu Dhir, and Nonita Sharma. 2022. “Land Use Land Cover Classification of Remote Sensing Images Based on the Deep Learning Approaches: A Statistical Analysis and Review.” Arabian Journal of Geosciences 15 (10): 1003. https://doi.org/10.1007/s12517-022-10246-8.
   Google Scholar
• Ding, Lei, Hao Tang, and Lorenzo Bruzzone. 2020. “LANet: Local Attention Embedding to Improve the Semantic Segmentation of Remote Sensing Images.” IEEE Transactions on Geoscience and Remote Sensing 59 (1): 426–435. https://doi.org/10.1109/TGRS.36.
   Web of Science ®Google Scholar
• Doersch, Carl, Abhinav Gupta, and Alexei A. Efros. 2015. “Unsupervised Visual Representation Learning by Context Prediction.” In Proceedings of the IEEE International Conference on Computer Vision, 1422–1430.
   Google Scholar
• Dosovitskiy, Alexey, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, et al. 2020. “An Image is Worth 16×16 Words: Transformers for Image Recognition at Scale.” arXiv preprint arXiv: 2010.11929.
   Google Scholar
• Drusch, Matthias, Umberto Del Bello, Sébastien Carlier, Olivier Colin, Veronica Fernandez, Ferran Gascon, Bianca Hoersch. 2012. “Sentinel-2: ESA's Optical High-Resolution Mission for GMES Operational Services.” Remote Sensing of Environment 120:25–36. https://doi.org/10.1016/j.rse.2011.11.026.
   Web of Science ®Google Scholar
• Everaerts, Jurgen. 2008. “The Use of Unmanned Aerial Vehicles (UAVs) for Remote Sensing and Mapping.” The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 37 (2008): 1187–1192.
   Google Scholar
• Feng, Jie, Jiantong Chen, Qigong Sun, Ronghua Shang, Xianghai Cao, Xiangrong Zhang, and Licheng Jiao. 2020. “Convolutional Neural Network Based on Bandwise-Independent Convolution and Hard Thresholding for Hyperspectral Band Selection.” IEEE Transactions on Cybernetics 51 (9): 4414–4428. https://doi.org/10.1109/TCYB.2020.3000725.
   Web of Science ®Google Scholar
• Feng, Wenqing, Haigang Sui, Weiming Huang, Chuan Xu, and Kaiqiang An. 2018. “Water Body Extraction From Very High-Resolution Remote Sensing Imagery Using Deep U-Net and a Superpixel-Based Conditional Random Field Model.” IEEE Geoscience and Remote Sensing Letters 16 (4): 618–622. https://doi.org/10.1109/LGRS.2018.2879492.
   Web of Science ®Google Scholar
• Fu, Jun, Jing Liu, Haijie Tian, Yong Li, Yongjun Bao, Zhiwei Fang, and Hanqing Lu. 2019. “Dual Attention Network for Scene Segmentation.” In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 3146–3154.
   Google Scholar
• Gao, Liang, Hui Liu, Minhang Yang, Long Chen, Yaling Wan, Zhengqing Xiao, and Yurong Qian. 2021. “STransFuse: Fusing Swin Transformer and Convolutional Neural Network for Remote Sensing Image Semantic Segmentation.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14:10990–11003. https://doi.org/10.1109/JSTARS.2021.3119654.
   Web of Science ®Google Scholar
• Graikos, Alexandros, Nikolay Malkin, Nebojsa Jojic, and Dimitris Samaras. 2022. “Diffusion Models as Plug-and-Play Priors.” arXiv preprint arXiv: 2206.09012.
   Google Scholar
• Grill, Jean-Bastien, Florian Strub, Florent Altché, Corentin Tallec, Pierre Richemond, Elena Buchatskaya, Carl Doersch. 2020. “Bootstrap Your Own Latent—A New Approach to Self-Supervised Learning.” Advances in Neural Information Processing Systems 33:21271–21284.
   Google Scholar
• Guerrero Tello, José Francisco, Mauro Coltelli, Maria Marsella, Angela Celauro, and José Antonio Palenzuela Baena. 2022. “Convolutional Neural Network Algorithms for Semantic Segmentation of Volcanic Ash Plumes Using Visible Camera Imagery.” Remote Sensing 14 (18): 4477. https://doi.org/10.3390/rs14184477.
   Web of Science ®Google Scholar
• Guo, Hongxiang, Guojin He, Wei Jiang, Ranyu Yin, Lei Yan, and Wanchun Leng. 2020. “A Multi-Scale Water Extraction Convolutional Neural Network (MWEN) Method for GaoFen-1 Remote Sensing Images.” ISPRS International Journal of Geo-Information 9 (4): 189. https://doi.org/10.3390/ijgi9040189.
   Web of Science ®Google Scholar
• Hamaguchi, Ryuhei, Aito Fujita, Keisuke Nemoto, Tomoyuki Imaizumi, and Shuhei Hikosaka. 2018. “Effective Use of Dilated Convolutions for Segmenting Small Object Instances in Remote Sensing Imagery.” In 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), 1442–1450.
   Google Scholar
• Hang, Renlong, Ping Yang, Feng Zhou, and Qingshan Liu. 2022. “Multiscale Progressive Segmentation Network for High-Resolution Remote Sensing Imagery.” IEEE Transactions on Geoscience and Remote Sensing 60: 1–12.
   Web of Science ®Google Scholar
• Hartling, Sean, Vasit Sagan, and Maitiniyazi Maimaitijiang. 2021. “Urban Tree Species Classification Using UAV-based Multi-Sensor Data Fusion and Machine Learning.” GIScience & Remote Sensing 58 (8): 1250–1275. https://doi.org/10.1080/15481603.2021.1974275.
   Web of Science ®Google Scholar
• He, Kaiming, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, and Ross Girshick. 2022. “Masked Autoencoders are Scalable Vision Learners.” In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 16000–16009.
   Google Scholar
• He, Kaiming, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick. 2020. “Momentum Contrast for Unsupervised Visual Representation Learning.” In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 9729–9738.
   Google Scholar
• He, Ke, Weiwei Sun, Gang Yang, Xiangchao Meng, Kai Ren, Jiangtao Peng, and Qian Du. 2022. “A Dual Global–local Attention Network for Hyperspectral Band Selection.” IEEE Transactions on Geoscience and Remote Sensing 60: 1–13.
   Google Scholar
• He, Kaiming, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. “Deep Residual Learning for Image Recognition.” In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 770–778.
   Google Scholar
• He, Ji, Lina Zhao, Hongwei Yang, Mengmeng Zhang, and Wei Li. 2019. “HSI-BERT: Hyperspectral Image Classification Using the Bidirectional Encoder Representation from Transformers.” IEEE Transactions on Geoscience and Remote Sensing 58 (1): 165–178. https://doi.org/10.1109/TGRS.36.
   Web of Science ®Google Scholar
• Hong, Danfeng, Lianru Gao, Naoto Yokoya, Jing Yao, Jocelyn Chanussot, Qian Du, and Bing Zhang. 2020. “More Diverse Means Better: Multimodal Deep Learning Meets Remote-Sensing Imagery Classification.” IEEE Transactions on Geoscience and Remote Sensing 59 (5): 4340–4354. https://doi.org/10.1109/TGRS.2020.3016820.
   Web of Science ®Google Scholar
• Hong, Danfeng, Zhu Han, Jing Yao, Lianru Gao, Bing Zhang, Antonio Plaza, and Jocelyn Chanussot. 2021. “SpectralFormer: Rethinking Hyperspectral Image Classification with Transformers.” IEEE Transactions on Geoscience and Remote Sensing 60:1–15. https://doi.org/10.1109/TGRS.2022.3172371.
   Web of Science ®Google Scholar
• Hong, Danfeng, Jing Yao, Chenyu Li, Deyu Meng, Naoto Yokoya, and Jocelyn Chanussot. 2023. “Decoupled-and-Coupled Networks: Self-Supervised Hyperspectral Image Super-Resolution With Subpixel Fusion.” IEEE Transactions on Geoscience and Remote Sensing 61:1–12. https://doi.org/10.1109/TGRS.2023.3324497.
   Web of Science ®Google Scholar
• Hong, Danfeng, Bing Zhang, Xuyang Li, Yuxuan Li, Chenyu Li, Jing Yao, Naoto Yokoya, et al. 2023. “Spectralgpt: Spectral Foundation Model.” arXiv preprint arXiv: 2311.07113.
   Google Scholar
• Hong, Danfeng, Bing Zhang, Hao Li, Yuxuan Li, Jing Yao, Chenyu Li, Martin Werner, et al. 2023. “Cross-City Matters: A Multimodal Remote Sensing Benchmark Dataset for Cross-City Semantic Segmentation Using High-Resolution Domain Adaptation Networks.” Remote Sensing of Environment299:113856. https://doi.org/10.1016/j.rse.2023.113856.
   Web of Science ®Google Scholar
• Hou, Sikang, Hongye Shi, Xianghai Cao, Xiaohua Zhang, and Licheng Jiao. 2021. “Hyperspectral Imagery Classification Based on Contrastive Learning.” IEEE Transactions on Geoscience and Remote Sensing 60:1–13.
   Google Scholar
• Hu, Jie, Li Shen, and Gang Sun. 2018. “Squeeze-and-Excitation Networks.” In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 7132–7141.
   Google Scholar
• Huang, Liwei, Bitao Jiang, Shouye Lv, Yanbo Liu, and Ying Fu. 2023. “Deep Learning-Based Semantic Segmentation of Remote Sensing Images: A Survey.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 1–28.
   Google Scholar
• Huang, Gao, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q. Weinberger. 2017. “Densely Connected Convolutional Networks.” In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 4700–4708.
   Google Scholar
• Huang, Zilong, Xinggang Wang, Lichao Huang, Chang Huang, Yunchao Wei, and Wenyu Liu. 2019. “Ccnet: Criss-Cross Attention for Semantic Segmentation.” In Proceedings of the IEEE/CVF International Conference on Computer Vision, 603–612.
   Google Scholar
• Isikdogan, Furkan, Alan C. Bovik, and Paola Passalacqua. 2017. “Surface Water Mapping by Deep Learning.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 10 (11): 4909–4918. https://doi.org/10.1109/JSTARS.4609443.
   Web of Science ®Google Scholar
• Jain, Deepak Kumar, Surendra Bilouhan Dubey, Rishin Kumar Choubey, Amit Sinhal, Siddharth Kumar Arjaria, Amar Jain, and Haoxiang Wang. 2018. “An Approach for Hyperspectral Image Classification by Optimizing SVM Using Self Organizing Map.” Journal of Computational Science 25:252–259. https://doi.org/10.1016/j.jocs.2017.07.016.
   Web of Science ®Google Scholar
• Jang, GyuJin, Jaeyoung Kim, Ju-Kyung Yu, Hak-Jin Kim, Yoonha Kim, Dong-Wook Kim, Kyung-Hwan Kim, Chang Woo Lee, and Yong Suk Chung. 2020. “Cost-Effective Unmanned Aerial Vehicle (UAV) Platform for Field Plant Breeding Application.” Remote Sensing 12 (6): 998. https://doi.org/10.3390/rs12060998.
   Web of Science ®Google Scholar
• Jiang, Wandong, Jiangbo Xi, Zhenhong Li, Minghui Zang, Bo Chen, Chenglong Zhang, Zhenjiang Liu, Siyan Gao, and Wu Zhu. 2022. “Deep Learning for Landslide Detection and Segmentation in High-Resolution Optical Images Along the Sichuan-Tibet Transportation Corridor.” Remote Sensing 14 (21): 5490. https://doi.org/10.3390/rs14215490.
   Web of Science ®Google Scholar
• Jiao, Rushi, Yichi Zhang, Le Ding, Rong Cai, and Jicong Zhang. 2022. “Learning with Limited Annotations: A Survey on Deep Semi-Supervised Learning for Medical Image Segmentation.” arXiv preprint arXiv: 2207.14191.
   Google Scholar
• Kampffmeyer, Michael, Arnt-Borre Salberg, and Robert Jenssen. 2016. “Semantic Segmentation of Small Objects and Modeling of Uncertainty in Urban Remote Sensing Images Using Deep Convolutional Neural Networks.” In 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 680–688.
   Google Scholar
• Keshk, Hatem Magdy, and Xu-Cheng Yin. 2017. “Satellite Super-Resolution Images Depending on Deep Learning Methods: A Comparative Study.” In 2017 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC), 1–7.
   Google Scholar
• Kim, Miae, Junghee Lee, Daehyeon Han, Minso Shin, Jungho Im, Junghye Lee, Lindi J. Quackenbush, and Zhu Gu. 2018. “Convolutional Neural Network-Based Land Cover Classification Using 2-D Spectral Reflectance Curve Graphs with Multitemporal Satellite Imagery.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 11 (12): 4604–4617. https://doi.org/10.1109/JSTARS.4609443.
   Web of Science ®Google Scholar
• Kipf, Thomas N., and Max Welling. 2016. “Semi-Supervised Classification with Graph Convolutional Networks.” arXiv preprint arXiv: 1609.02907.
   Google Scholar
• Kirillov, Alexander, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, et al. 2023. “Segment Anything.” arXiv preprint arXiv: 2304.02643.
   Google Scholar
• Kolbeinsson, Benedikt, and Krystian Mikolajczyk. 2022. “Multi-Class Segmentation from Aerial Views using Recursive Noise Diffusion.” arXiv preprint arXiv: 2212.00787.
   Google Scholar
• Kulwa, Frank, Chen Li, Marcin Grzegorzek, Md Mamunur Rahaman, Kimiaki Shirahama, and Sergey Kosov. 2023. “Segmentation of Weakly Visible Environmental Microorganism Images Using Pair-Wise Deep Learning Features.” Biomedical Signal Processing and Control 79:104168. https://doi.org/10.1016/j.bspc.2022.104168.
   Web of Science ®Google Scholar
• Kupidura, Przemysław. 2019. “The Comparison of Different Methods of Texture Analysis for Their Efficacy for Land Use Classification in Satellite Imagery.” Remote Sensing 11 (10): 1233. https://doi.org/10.3390/rs11101233.
   Web of Science ®Google Scholar
• Kussul, Nataliia, Mykola Lavreniuk, Sergii Skakun, and Andrii Shelestov. 2017. “Deep Learning Classification of Land Cover and Crop Types Using Remote Sensing Data.” IEEE Geoscience and Remote Sensing Letters 14 (5): 778–782. https://doi.org/10.1109/LGRS.2017.2681128.
   Web of Science ®Google Scholar
• LeCun, Yann, Léon Bottou, Yoshua Bengio, and Patrick Haffner. 1998. “Gradient-Based Learning Applied to Document Recognition.” Proceedings of the IEEE 86 (11): 2278–2324. https://doi.org/10.1109/5.726791.
   Web of Science ®Google Scholar
• Li, Wenyuan, Hao Chen, and Zhenwei Shi. 2021. “Semantic Segmentation of Remote Sensing Images with Self-Supervised Multitask Representation Learning.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14:6438–6450. https://doi.org/10.1109/JSTARS.2021.3090418.
   Web of Science ®Google Scholar
• Li, Aimin, Meng Fan, Guangduo Qin, Youcheng Xu, and Hailong Wang. 2021. “Comparative Analysis of Machine Learning Algorithms in Automatic Identification and Extraction of Water Boundaries.” Applied Sciences 11 (21): 10062. https://doi.org/10.3390/app112110062.
   Google Scholar
• Li, Haifeng, Yi Li, Guo Zhang, Ruoyun Liu, Haozhe Huang, Qing Zhu, and Chao Tao. 2022. “Global and Local Contrastive Self-Supervised Learning for Semantic Segmentation of HR Remote Sensing Images.” IEEE Transactions on Geoscience and Remote Sensing 60:1–14.
   Web of Science ®Google Scholar
• Li, Junjie, Yizhuo Meng, Yuanxi Li, Qian Cui, Xining Yang, Chongxin Tao, Zhe Wang, Linyi Li, and Wen Zhang. 2022. “Accurate Water Extraction Using Remote Sensing Imagery Based on Normalized Difference Water Index and Unsupervised Deep Learning.” Journal of Hydrology 612: 128202. https://doi.org/10.1016/j.jhydrol.2022.128202.
   Web of Science ®Google Scholar
• Li, Liwei, Zhi Yan, Qian Shen, Gang Cheng, Lianru Gao, and Bing Zhang. 2019. “Water Body Extraction From Very High Spatial Resolution Remote Sensing Data Based on Fully Convolutional Networks.” Remote Sensing 11 (10): 1162. https://doi.org/10.3390/rs11101162.
   Web of Science ®Google Scholar
• Li, Jiangyun, Sen Zha, Chen Chen, Meng Ding, Tianxiang Zhang, and Hong Yu. 2022. “Attention Guided Global Enhancement and Local Refinement Network for Semantic Segmentation.” IEEE Transactions on Image Processing 31:3211–3223. https://doi.org/10.1109/TIP.2022.3166673.
   PubMed Web of Science ®Google Scholar
• Li, Ying, Haokui Zhang, Xizhe Xue, Yenan Jiang, and Qiang Shen. 2018. “Deep Learning for Remote Sensing Image Classification: A Survey.” Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 8 (6): e1264.
   Web of Science ®Google Scholar
• Li, Linhui, Wenjun Zhang, Xiaoyan Zhang, Mahmoud Emam, and Weipeng Jing. 2023. “Semi-Supervised Remote Sensing Image Semantic Segmentation Method Based on Deep Learning.” Electronics 12 (2): 348. https://doi.org/10.3390/electronics12020348.
   Web of Science ®Google Scholar
• Li, Rui, Shunyi Zheng, Ce Zhang, Chenxi Duan, Jianlin Su, Libo Wang, and Peter M. Atkinson. 2021. “Multiattention Network for Semantic Segmentation of Fine-Resolution Remote Sensing Images.” IEEE Transactions on Geoscience and Remote Sensing 60:1–13.
   Web of Science ®Google Scholar
• Lin, Tsung-Yi, Piotr Dollár, Ross Girshick, Kaiming He, Bharath Hariharan, and Serge Belongie. 2017. “Feature Pyramid Networks for Object Detection.” In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2117–2125.
   Google Scholar
• Liu, Ze, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo. 2021. “Swin Transformer: Hierarchical Vision Transformer Using Shifted Windows.” In Proceedings of the IEEE/CVF International Conference on Computer Vision, 10012–10022.
   Google Scholar
• Liu, Jun, and Xuewei Wang. 2021. “Plant Diseases and Pests Detection Based on Deep Learning: A Review.” Plant Methods 17 (1): 1–18. https://doi.org/10.1186/s13007-020-00700-7.
   PubMed Web of Science ®Google Scholar
• Liu, Bing, Anzhu Yu, Kuiliang Gao, Xiong Tan, Yifan Sun, and Xuchu Yu. 2022. “DSS-TRM: Deep Spatial–spectral Transformer for Hyperspectral Image Classification.” European Journal of Remote Sensing 55 (1): 103–114. https://doi.org/10.1080/22797254.2021.2023910.
   Web of Science ®Google Scholar
• Liu, Wenjie, Yongjun Zhang, Haisheng Fan, Yongjie Zou, and Zhongwei Cui. 2020. “A New Multi-Channel Deep Convolutional Neural Network for Semantic Segmentation of Remote Sensing Image.” IEEE Access 8: 131814–131825. https://doi.org/10.1109/Access.6287639.
   Google Scholar
• Liu, Yifan, Qigang Zhu, Feng Cao, Junke Chen, and Gang Lu. 2021. “High-Resolution Remote Sensing Image Segmentation Framework Based on Attention Mechanism and Adaptive Weighting.” ISPRS International Journal of Geo-Information 10 (4): 241. https://doi.org/10.3390/ijgi10040241.
   Web of Science ®Google Scholar
• Long, Jonathan, Evan Shelhamer, and Trevor Darrell. 2015. “Fully Convolutional Networks for Semantic Segmentation.” In Proceedings of the IEEE conference on computer vision and pattern recognition, 3431–3440.
   Google Scholar
• Lv, Jinna, Qi Shen, Mingzheng Lv, Yiran Li, Lei Shi, and Peiying Zhang. 2023. “Deep Learning-Based Semantic Segmentation of Remote Sensing Images: A Review.” Frontiers in Ecology and Evolution 11: 1201125. https://doi.org/10.3389/fevo.2023.1201125.
   Web of Science ®Google Scholar
• Ma, Lei, Manchun Li, Xiaoxue Ma, Liang Cheng, Peijun Du, and Yongxue Liu. 2017. “A Review of Supervised Object-Based Land-Cover Image Classification.” ISPRS Journal of Photogrammetry and Remote Sensing 130:277–293. https://doi.org/10.1016/j.isprsjprs.2017.06.001.
   Web of Science ®Google Scholar
• Ma, Zhengjing, and Gang Mei. 2021. “Deep Learning for Geological Hazards Analysis: Data, Models, Applications, and Opportunities.” Earth-Science Reviews 223: 103858. https://doi.org/10.1016/j.earscirev.2021.103858.
   Web of Science ®Google Scholar
• Manisalidis, Ioannis, Elisavet Stavropoulou, Agathangelos Stavropoulos, and Eugenia Bezirtzoglou. 2020. “Environmental and Health Impacts of Air Pollution: A Review.” Frontiers in Public Health 8:14. https://doi.org/10.3389/fpubh.2020.00014.
   PubMed Web of Science ®Google Scholar
• Martins, José Augusto Correa, Keiller Nogueira, Lucas Prado Osco, Felipe David Georges Gomes, Danielle Elis Garcia Furuya, Wesley Nunes Gonçalves, Diego André Sant'Ana. 2021. “Semantic Segmentation of Tree-Canopy in Urban Environment with Pixel-Wise Deep Learning.” Remote Sensing 13 (16): 3054. https://doi.org/10.3390/rs13163054.
   Web of Science ®Google Scholar
• Masek, Jeffrey G., Eric F. Vermote, Nazmi E. Saleous, Robert Wolfe, Forrest G. Hall, Karl Fred Huemmrich, Feng Gao, Jonathan Kutler, and Teng-Kui Lim. 2006. “A Landsat Surface Reflectance Dataset for North America, 1990–2000.” IEEE Geoscience and Remote Sensing Letters 3 (1): 68–72. https://doi.org/10.1109/LGRS.2005.857030.
   Web of Science ®Google Scholar
• Mou, Lichao, Pedram Ghamisi, and Xiao Xiang Zhu. 2017. “Unsupervised Spectral–Spatial Feature Learning Via Deep Residual Conv–Deconv Network for Hyperspectral Image Classification.” IEEE Transactions on Geoscience and Remote Sensing 56 (1): 391–406. https://doi.org/10.1109/TGRS.2017.2748160.
   Web of Science ®Google Scholar
• Naseer, Muhammad Muzammal, Kanchana Ranasinghe, Salman H. Khan, Munawar Hayat, Fahad Shahbaz Khan, and Ming-Hsuan Yang. 2021. “Intriguing Properties of Vision Transformers.” Advances in Neural Information Processing Systems 34:23296–23308.
   Google Scholar
• Neupane, Bipul, Teerayut Horanont, and Jagannath Aryal. 2021. “Deep Learning-Based Semantic Segmentation of Urban Features in Satellite Images: A Review and Meta-Analysis.” Remote Sensing 13 (4): 808. https://doi.org/10.3390/rs13040808.
   Web of Science ®Google Scholar
• Nogueira, Keiller, Mauro Dalla Mura, Jocelyn Chanussot, William Robson Schwartz, and Jefersson Alex Dos Santos. 2019. “Dynamic Multicontext Segmentation of Remote Sensing Images Based on Convolutional Networks.” IEEE Transactions on Geoscience and Remote Sensing 57 (10): 7503–7520. https://doi.org/10.1109/TGRS.36.
   Web of Science ®Google Scholar
• Noor, Norzailawati Mohd, Alias Abdullah, and Mazlan Hashim. 2018. “Remote Sensing UAV/Drones and Its Applications for Urban Areas: A Review.” In IOP Conference Series: Earth and Environmental Science, 012003.
   Google Scholar
• Noroozi, Mehdi, and Paolo Favaro. 2016. “Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles.” In Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part VI, 69–84.
   Google Scholar
• Osco, Lucas Prado, Mauro dos Santos De Arruda, José Marcato Junior, Neemias Buceli Da Silva, Ana Paula Marques Ramos, Érika Akemi Saito Moryia, Nilton Nobuhiro Imai. 2020. “A Convolutional Neural Network Approach for Counting and Geolocating Citrus-Trees in UAV Multispectral Imagery.” ISPRS Journal of Photogrammetry and Remote Sensing 160:97–106. https://doi.org/10.1016/j.isprsjprs.2019.12.010.
   Web of Science ®Google Scholar
• Pan, Qian, Maofang Gao, Pingbo Wu, Jingwen Yan, and Shilei Li. 2021. “A Deep-Learning-based Approach for Wheat Yellow Rust Disease Recognition From Unmanned Aerial Vehicle Images.” Sensors 21 (19): 6540. https://doi.org/10.3390/s21196540.
   PubMed Web of Science ®Google Scholar
• Pan, Yifan, Xianfeng Zhang, Guido Cervone, and Liping Yang. 2018. “Detection of Asphalt Pavement Potholes and Cracks Based on the Unmanned Aerial Vehicle Multispectral Imagery.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 11 (10): 3701–3712. https://doi.org/10.1109/JSTARS.4609443.
   Web of Science ®Google Scholar
• Panboonyuen, Teerapong, Kulsawasd Jitkajornwanich, Siam Lawawirojwong, Panu Srestasathiern, and Peerapon Vateekul. 2019. “Semantic Segmentation on Remotely Sensed Images Using An Enhanced Global Convolutional Network with Channel Attention and Domain Specific Transfer Learning.” Remote Sensing 11 (1): 83. https://doi.org/10.3390/rs11010083.
   Web of Science ®Google Scholar
• Park, Namuk, and Songkuk Kim. 2022. “How Do Vision Transformers Work?” arXiv preprint arXiv: 2202.06709.
   Google Scholar
• Ramanathan, Veerabhadran, and Yan Feng. 2009. “Air Pollution, Greenhouse Gases and Climate Change: Global and Regional Perspectives.” Atmospheric Environment 43 (1): 37–50. https://doi.org/10.1016/j.atmosenv.2008.09.063.
   Web of Science ®Google Scholar
• Ronneberger, Olaf, Philipp Fischer, and Thomas Brox. 2015. “U-Net: Convolutional Networks for Biomedical Image Segmentation.” In Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany, October 5–9, 2015, Proceedings, Part III 18, 234–241.
   Google Scholar
• Scheibenreif, Linus, Michael Mommert, and Damian Borth. 2022. “Toward Global Estimation of Ground-Level No 2 Pollution with Deep Learning and Remote Sensing.” IEEE Transactions on Geoscience and Remote Sensing 60:1–14. https://doi.org/10.1109/TGRS.2022.3160827.
   Google Scholar
• Shao, Zhenfeng, Yin Pan, Chunyuan Diao, and Jiajun Cai. 2019. “Cloud Detection in Remote Sensing Images Based on Multiscale Features-Convolutional Neural Network.” IEEE Transactions on Geoscience and Remote Sensing 57 (6): 4062–4076. https://doi.org/10.1109/TGRS.36.
   Web of Science ®Google Scholar
• Shi, Di, and Xiaojun Yang. 2016. “An Assessment of Algorithmic Parameters Affecting Image Classification Accuracy by Random Forests.” Photogrammetric Engineering & Remote Sensing 82 (6): 407–417. https://doi.org/10.14358/PERS.82.6.407.
   Web of Science ®Google Scholar
• Su, Jinya, Dewei Yi, Baofeng Su, Zhiwen Mi, Cunjia Liu, Xiaoping Hu, Xiangming Xu, Lei Guo, and Wen-Hua Chen. 2021. “Aerial Visual Perception in Smart Farming: Field Study of Wheat Yellow Rust Monitoring.” IEEE Transactions on Industrial Informatics 17 (3): 2242–2249. https://doi.org/10.1109/TII.9424.
   Web of Science ®Google Scholar
• Su, Jinya, Xiaoyong Zhu, Shihua Li, and Wen-Hua Chen. 2023. “AI Meets UAVs: A Survey on AI Empowered UAV Perception Systems for Precision Agriculture.” Neurocomputing 518:242–270. https://doi.org/10.1016/j.neucom.2022.11.020.
   Web of Science ®Google Scholar
• Sun, Xian, Peijin Wang, Wanxuan Lu, Zicong Zhu, Xiaonan Lu, Qibin He, Junxi Li. 2023. “RingMo: A Remote Sensing Foundation Model With Masked Image Modeling.” IEEE Transactions on Geoscience and Remote Sensing 61:1–22.
   Web of Science ®Google Scholar
• Sun, Le, Guangrui Zhao, Yuhui Zheng, and Zebin Wu. 2022. “Spectral–spatial Feature Tokenization Transformer for Hyperspectral Image Classification.” IEEE Transactions on Geoscience and Remote Sensing 60:1–14.
   Google Scholar
• Talukdar, Swapan, Pankaj Singha, Susanta Mahato, Swades Pal, Yuei-An Liou, and Atiqur Rahman. 2020. “Land-Use Land-Cover Classification by Machine Learning Classifiers for Satellite Observations—A Review.” Remote Sensing 12 (7): 1135. https://doi.org/10.3390/rs12071135.
   Web of Science ®Google Scholar
• Tarvainen, Antti, and Harri Valpola. 2017. “Mean Teachers are Better Role Models: Weight-Averaged Consistency Targets Improve Semi-Supervised Deep Learning Results.” Advances in Neural Information Processing Systems 30: 1195–1204.
   Google Scholar
• Vaswani, Ashish, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. “Attention is All You Need.” Advances in Neural Information Processing Systems 30: 5998–6008.
   Google Scholar
• Vincenzi, Stefano, Angelo Porrello, Pietro Buzzega, Marco Cipriano, Pietro Fronte, Roberto Cuccu, Carla Ippoliti, Annamaria Conte, and Simone Calderara. 2021. “The Color Out of Space: Learning Self-Supervised Representations for Earth Observation Imagery.” In 2020 25th International Conference on Pattern Recognition (ICPR), 3034–3041.
   Google Scholar
• Viskovic, Lucija, Ivana Nizetic Kosovic, and Toni Mastelic. 2019. “Crop Classification Using Multi-Spectral and Multitemporal Satellite Imagery with Machine Learning.” In 2019 International Conference on Software, Telecommunications and Computer Networks (SoftCOM), 1–5.
   Google Scholar
• Wang, Yi, Conrad M. Albrecht, Nassim Ait Ali Braham, Lichao Mou, and Xiao Xiang Zhu. 2022. “Self-Supervised Learning in Remote Sensing: A Review.” arXiv preprint arXiv: 2206.13188.
   Google Scholar
• Wang, Jia-Xin, Si-Bao Chen, Chris H. Q. Ding, Jin Tang, and Bin Luo. 2022. “Semi-Supervised Semantic Segmentation of Remote Sensing Images with Iterative Contrastive Network.” IEEE Geoscience and Remote Sensing Letters 19:1–5.
   Web of Science ®Google Scholar
• Wang, Hong, Xianzhong Chen, Tianxiang Zhang, Zhiyong Xu, and Jiangyun Li. 2022. “CCTNet: Coupled CNN and Transformer Network for Crop Segmentation of Remote Sensing Images.” Remote Sensing 14 (9): 1956. https://doi.org/10.3390/rs14091956.
   Web of Science ®Google Scholar
• Wang, Jiaxin, Chris H. Q. Ding, Sibao Chen, Chenggang He, and Bin Luo. 2020. “Semi-Supervised Remote Sensing Image Semantic Segmentation Via Consistency Regularization and Average Update of Pseudo-Label.” Remote Sensing 12 (21): 3603. https://doi.org/10.3390/rs12213603.
   Web of Science ®Google Scholar
• Wang, Xiaolong, Ross Girshick, Abhinav Gupta, and Kaiming He. 2018. “Non-Local Neural Networks.” In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 7794–7803.
   Google Scholar
• Wang, Libo, Rui Li, Chenxi Duan, Ce Zhang, Xiaoliang Meng, and Shenghui Fang. 2022. “A Novel Transformer Based Semantic Segmentation Scheme for Fine-Resolution Remote Sensing Images.” IEEE Geoscience and Remote Sensing Letters 19:1–5.
   Web of Science ®Google Scholar
• Wang, Libo, Rui Li, Ce Zhang, Shenghui Fang, Chenxi Duan, Xiaoliang Meng, and Peter M. Atkinson. 2022. “UNetFormer: A UNet-Like Transformer for Efficient Semantic Segmentation of Remote Sensing Urban Scene Imagery.” ISPRS Journal of Photogrammetry and Remote Sensing 190:196–214. https://doi.org/10.1016/j.isprsjprs.2022.06.008.
   Web of Science ®Google Scholar
• Wang, Wenxuan, Leiming Liu, Tianxiang Zhang, Jiachen Shen, Jing Wang, and Jiangyun Li. 2022. “Hyper-ES2T: Efficient Spatial–spectral Transformer for the Classification of Hyperspectral Remote Sensing Images.” International Journal of Applied Earth Observation and Geoinformation 113:103005. https://doi.org/10.1016/j.jag.2022.103005.
   Web of Science ®Google Scholar
• Wang, Lijun, Jiayao Wang, Xiwang Zhang, Laigang Wang, and Fen Qin. 2022. “Deep Segmentation and Classification of Complex Crops Using Multi-Feature Satellite Imagery.” Computers and Electronics in Agriculture 200:107249. https://doi.org/10.1016/j.compag.2022.107249.
   Web of Science ®Google Scholar
• Wang, Guojie, Mengjuan Wu, Xikun Wei, and Huihui Song. 2020. “Water Identification From High-Resolution Remote Sensing Images Based on Multidimensional Densely Connected Convolutional Neural Networks.” Remote Sensing 12 (5): 795. https://doi.org/10.3390/rs12050795.
   Web of Science ®Google Scholar
• Wang, Wenhai, Enze Xie, Xiang Li, Deng-Ping Fan, Kaitao Song, Ding Liang, Tong Lu, Ping Luo, and Ling Shao. 2021. “Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction Without Convolutions.” In Proceedings of the IEEE/CVF International Conference on Computer Vision, 568–578.
   Google Scholar
• Wójtowicz, Marek, Andrzej Wójtowicz, Jan Piekarczyk. 2016. “Application of Remote Sensing Methods in Agriculture.” Communications in Biometry and Crop Science 11 (1): 31–50.
   Google Scholar
• Wolleb, Julia, Robin Sandkühler, Florentin Bieder, Philippe Valmaggia, and Philippe C. Cattin. 2022. “Diffusion Models for Implicit Image Segmentation Ensembles.” In International Conference on Medical Imaging with Deep Learning, 1336–1348.
   Google Scholar
• Wu, Junde, Huihui Fang, Yu Zhang, Yehui Yang, and Yanwu Xu. 2022. “MedSegDiff: Medical Image Segmentation with Diffusion Probabilistic Model.” arXiv preprint arXiv: 2211.00611.
   Google Scholar
• Wu, Junde, Rao Fu, Huihui Fang, Yu Zhang, and Yanwu Xu. 2023. “MedSegDiff-V2: Diffusion based Medical Image Segmentation with Transformer.” arXiv preprint arXiv: 2301.11798.
   Google Scholar
• Wurm, Michael, Thomas Stark, Xiao Xiang Zhu, Matthias Weigand, and Hannes Taubenböck. 2019. “Semantic Segmentation of Slums in Satellite Images Using Transfer Learning on Fully Convolutional Neural Networks.” ISPRS Journal of Photogrammetry and Remote Sensing 150:59–69. https://doi.org/10.1016/j.isprsjprs.2019.02.006.
   Web of Science ®Google Scholar
• Xiang, Deqiang, Xin Zhang, Wei Wu, and Hongbin Liu. 2023. “Denseppmunet-a: A Robust Deep Learning Network for Segmenting Water Bodies From Aerial Images.” IEEE Transactions on Geoscience and Remote Sensing 61:1–11.
   Web of Science ®Google Scholar
• Xiao, Yi, Yahui Guo, Guodong Yin, Xuan Zhang, Yu Shi, Fanghua Hao, and Yongshuo Fu. 2022. “UAV Multispectral Image-Based Urban River Water Quality Monitoring Using Stacked Ensemble Machine Learning Algorithms—A Case Study of the Zhanghe River, China.” Remote Sensing 14 (14): 3272. https://doi.org/10.3390/rs14143272.
   Web of Science ®Google Scholar
• Xiao, Tao, Yikun Liu, Yuwen Huang, Mingsong Li, and Gongping Yang. 2023. “Enhancing Multiscale Representations with Transformer for Remote Sensing Image Semantic Segmentation.” IEEE Transactions on Geoscience and Remote Sensing 61:1–16.
   Web of Science ®Google Scholar
• Xiong, Chen, Qiangsheng Li, and Xinzheng Lu. 2020. “Automated Regional Seismic Damage Assessment of Buildings Using An Unmanned Aerial Vehicle and a Convolutional Neural Network.” Automation in Construction 109:102994. https://doi.org/10.1016/j.autcon.2019.102994.
   Web of Science ®Google Scholar
• Xu, Yizhe, and Jie Jiang. 2022. “High-Resolution Boundary-Constrained and Context-Enhanced Network for Remote Sensing Image Segmentation.” Remote Sensing 14 (8): 1859. https://doi.org/10.3390/rs14081859.
   Web of Science ®Google Scholar
• Xu, Zhiyong, Weicun Zhang, Tianxiang Zhang, and Jiangyun Li. 2020. “HRCNet: High-Resolution Context Extraction Network for Semantic Segmentation of Remote Sensing Images.” Remote Sensing 13 (1): 71. https://doi.org/10.3390/rs13010071.
   Web of Science ®Google Scholar
• Xu, Zhiyong, Weicun Zhang, Tianxiang Zhang, Zhifang Yang, and Jiangyun Li. 2021. “Efficient Transformer for Remote Sensing Image Segmentation.” Remote Sensing 13 (18): 3585. https://doi.org/10.3390/rs13183585.
   Web of Science ®Google Scholar
• Yang, Xiaofei, Weijia Cao, Yao Lu, and Yicong Zhou. 2022. “Hyperspectral Image Transformer Classification Networks.” IEEE Transactions on Geoscience and Remote Sensing 60:1–15.
   Google Scholar
• Yao, Jing, Bing Zhang, Chenyu Li, Danfeng Hong, and Jocelyn Chanussot. 2023. “Extended Vision Transformer (ExViT) for Land Use and Land Cover Classification: A Multimodal Deep Learning Framework.” IEEE Transactions on Geoscience and Remote Sensing 61:1–15.
   Web of Science ®Google Scholar
• Yao, Min, Yaozu Zhang, Guofeng Liu, and Dongdong Pang. 2024. “SSNet: A Novel Transformer and CNN Hybrid Network for Remote Sensing Semantic Segmentation.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 17:3023–3037. https://doi.org/10.1109/JSTARS.2024.3349657.
   Web of Science ®Google Scholar
• Yasir, Muhammad, Wan Jianhua, Liu Shanwei, Hui Sheng, Xu Mingming, and Md Hossain. 2023. “Coupling of Deep Learning and Remote Sensing: A Comprehensive Systematic Literature Review.” International Journal of Remote Sensing 44 (1): 157–193. https://doi.org/10.1080/01431161.2022.2161856.
   Web of Science ®Google Scholar
• Ye, Huanran, Sheng Liu, Kun Jin, and Haohao Cheng. 2021. “CT-UNet: An Improved Neural Network Based on U-Net for Building Segmentation in Remote Sensing Images.” In 2020 25th International Conference on Pattern Recognition (ICPR), 166–172.
   Google Scholar
• Yu, Fisher, and Vladlen Koltun. 2015. “Multi-Scale Context Aggregation by Dilated Convolutions.” arXiv preprint arXiv: 1511.07122.
   Google Scholar
• Yu, Long, Zhiyin Wang, Shengwei Tian, Feiyue Ye, Jianli Ding, and Jun Kong. 2017. “Convolutional Neural Networks for Water Body Extraction From Landsat Imagery.” International Journal of Computational Intelligence and Applications 16 (1): 1750001. https://doi.org/10.1142/S1469026817500018.
   Web of Science ®Google Scholar
• Yuan, Yuhui, Xilin Chen, and Jingdong Wang. 2020. “Object-Contextual Representations for Semantic Segmentation.” In Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part VI 16, 173–190.
   Google Scholar
• Yuan, Xiaohui, Jianfang Shi, and Lichuan Gu. 2021. “A Review of Deep Learning Methods for Semantic Segmentation of Remote Sensing Imagery.” Expert Systems with Applications 169:114417. https://doi.org/10.1016/j.eswa.2020.114417.
   Web of Science ®Google Scholar
• Yuan, Jiangye, DeLiang Wang, and Rongxing Li. 2014. “Remote Sensing Image Segmentation by Combining Spectral and Texture Features.” IEEE Transactions on Geoscience and Remote Sensing 52 (1): 16–24. https://doi.org/10.1109/TGRS.2012.2234755.
   Web of Science ®Google Scholar
• Yuan, Kunhao, Xu Zhuang, Gerald Schaefer, Jianxin Feng, Lin Guan, and Hui Fang. 2021. “Deep-Learning-based Multispectral Satellite Image Segmentation for Water Body Detection.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14: 7422–7434. https://doi.org/10.1109/JSTARS.2021.3098678.
   Web of Science ®Google Scholar
• Zang, Ning, Yun Cao, Yuebin Wang, Bo Huang, Liqiang Zhang, and P. Takis Mathiopoulos. 2021. “Land-Use Mapping for High-Spatial Resolution Remote Sensing Image Via Deep Learning: A Review.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14:5372–5391. https://doi.org/10.1109/JSTARS.2021.3078631.
   Web of Science ®Google Scholar
• Zhang, Tianxiang, Yuanxiu Cai, Peixian Zhuang, and Jiangyun Li. 2024. “Remotely Sensed Crop Disease Monitoring by Machine Learning Algorithms: A Review.” Unmanned Systems 12 (01): 161–171.
   Web of Science ®Google Scholar
• Zhang, Hang, Kristin Dana, Jianping Shi, Zhongyue Zhang, Xiaogang Wang, Ambrish Tyagi, and Amit Agrawal. 2018. “Context Encoding for Semantic Segmentation.” In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 7151–7160.
   Google Scholar
• Zhang, Fan, Bo Du, Liangpei Zhang, and Lefei Zhang. 2016. “Hierarchical Feature Learning with Dropout K-Means for Hyperspectral Image Classification.” Neurocomputing 187: 75–82. https://doi.org/10.1016/j.neucom.2015.07.132.
   Web of Science ®Google Scholar
• Zhang, Xin, Liangxiu Han, Yingying Dong, Yue Shi, Wenjiang Huang, Lianghao Han, Pablo González-Moreno, et al. 2019. “A Deep Learning-Based Approach for Automated Yellow Rust Disease Detection From High-Resolution Hyperspectral UAV Images.” Remote Sensing 11 (13): 1554. https://doi.org/10.3390/rs11131554.
   Web of Science ®Google Scholar
• Zhang, Cheng, Wanshou Jiang, Yuan Zhang, Wei Wang, Qing Zhao, and Chenjie Wang. 2022. “Transformer and CNN Hybrid Deep Neural Network for Semantic Segmentation of Very-High-resolution Remote Sensing Imagery.” IEEE Transactions on Geoscience and Remote Sensing 60:1–20.
   Web of Science ®Google Scholar
• Zhang, Deyuan, Zhenghong Liu, and Xiangbin Shi. 2020. “Transfer Learning on Efficientnet for Remote Sensing Image Classification.” In 2020 5th International Conference on Mechanical, Control and Computer Engineering (ICMCCE), 2255–2258.
   Google Scholar
• Zhang, Zhengxin, Qingjie Liu, and Yunhong Wang. 2018. “Road Extraction by Deep Residual U-Net.” IEEE Geoscience and Remote Sensing Letters 15 (5): 749–753. https://doi.org/10.1109/LGRS.2018.2802944.
   Web of Science ®Google Scholar
• Zhang, Le, Jinsong Wang, and Zhiyong An. 2020. “Classification Method of CO2 Hyperspectral Remote Sensing Data Based on Neural Network.” Computer Communications 156:124–130. https://doi.org/10.1016/j.comcom.2020.03.045.
   Web of Science ®Google Scholar
• Zhang, Tianxiang, Wenxuan Wang, Jing Wang, Yuanxiu Cai, Zhifang Yang, and Jiangyun Li. 2022. “Hyper-LGNet: Coupling Local and Global Features for Hyperspectral Image Classification.” Remote Sensing 14 (20): 5251. https://doi.org/10.3390/rs14205251.
   Web of Science ®Google Scholar
• Zhang, Tianxiang, Zhiyong Xu, Jinya Su, Zhifang Yang, Cunjia Liu, Wen-Hua Chen, and Jiangyun Li. 2021. “Ir-unet: Irregular Segmentation U-Shape Network for Wheat Yellow Rust Detection by UAV Multispectral Imagery.” Remote Sensing 13 (19): 3892. https://doi.org/10.3390/rs13193892.
   Web of Science ®Google Scholar
• Zhang, Ning, Guijun Yang, Yuchun Pan, Xiaodong Yang, Liping Chen, and Chunjiang Zhao. 2020. “A Review of Advanced Technologies and Development for Hyperspectral-Based Plant Disease Detection in the Past Three Decades.” Remote Sensing 12 (19): 3188. https://doi.org/10.3390/rs12193188.
   Web of Science ®Google Scholar
• Zhang, Tianxiang, Zhifang Yang, Zhiyong Xu, and Jiangyun Li. 2022. “Wheat Yellow Rust Severity Detection by Efficient DF-UNet and UAV Multispectral Imagery.” IEEE Sensors Journal 22 (9): 9057–9068. https://doi.org/10.1109/JSEN.2022.3156097.
   Web of Science ®Google Scholar
• Zhang, Liangpei, Lefei Zhang, Dacheng Tao, and Xin Huang. 2012. “Tensor Discriminative Locality Alignment for Hyperspectral Image Spectral–spatial Feature Extraction.” IEEE Transactions on Geoscience and Remote Sensing 51 (1): 242–256. https://doi.org/10.1109/TGRS.2012.2197860.
   Web of Science ®Google Scholar
• Zhao, Zhicheng, Ze Luo, Jian Li, Can Chen, and Yingchao Piao. 2020. “When Self-Supervised Learning Meets Scene Classification: Remote Sensing Scene Classification Based on a Multitask Learning Framework.” Remote Sensing 12 (20): 3276. https://doi.org/10.3390/rs12203276.
   Web of Science ®Google Scholar
• Zhao, Hengshuang, Jianping Shi, Xiaojuan Qi, Xiaogang Wang, and Jiaya Jia. 2017. “Pyramid Scene Parsing Network.” In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2881–2890.
   Google Scholar
• Zhong, Zilong, Ying Li, Lingfei Ma, Jonathan Li, and Wei-Shi Zheng. 2021. “Spectral–spatial Transformer Network for Hyperspectral Image Classification: A Factorized Architecture Search Framework.” IEEE Transactions on Geoscience and Remote Sensing 60:1–15. https://doi.org/10.1109/TGRS.2022.3225267.
   Web of Science ®Google Scholar
• Zhou, Yongxiu, Honghui Wang, Ronghao Yang, Guangle Yao, Qiang Xu, and Xiaojuan Zhang. 2022. “A Novel Weakly Supervised Remote Sensing Landslide Semantic Segmentation Method: Combining CAM and CycleGAN Algorithms.” Remote Sensing 14 (15): 3650. https://doi.org/10.3390/rs14153650.
   Web of Science ®Google Scholar
• Zhou, Shaoqing, Xiaoman Zhang, Shiwei Chu, Tiantian Zhang, and Junfei Wang. 2023. “Research on Remote Sensing Image Carbon Emission Monitoring Based on Deep Learning.” Signal Processing 207: 108943. https://doi.org/10.1016/j.sigpro.2023.108943.
   Web of Science ®Google Scholar
• Zou, Xueyan, Jianwei Yang, Hao Zhang, Feng Li, Linjie Li, Jianfeng Gao, and Yong Jae Lee. 2023. “Segment Everything Everywhere All At Once.” arXiv preprint arXiv: 2304.06718.
   Google Scholar
• Zou, Yuliang, Zizhao Zhang, Han Zhang, Chun-Liang Li, Xiao Bian, Jia-Bin Huang, and Tomas Pfister. 2020. “Pseudoseg: Designing Pseudo Labels for Semantic Segmentation.” arXiv preprint arXiv: 2010.09713.
   Google Scholar