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The development of high-resolution remote sensing satellites has provided great convenience for research work related to remote sensing.
Schiewe and J., “Segmentation of high-resolution remotely sensed data-concepts, ap- plications and problems,” Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 34, 380–385. , 2002
2002
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F. Melgani and L. Bruzzone, “Classification of hyperspectral remote sensing images with support vector machines,” IEEE Transactions on geoscience and remote sensing , vol. 42, no. 8, pp. 1778–1790, 2004
2004
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M. Pal and P. M. Mather, “Support vector machines for classification in remote sensing,” International journal of remote sensing , vol. 26, no. 5, pp. 1007–1011, 2005
2005
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T. Blaschke, “Object based image analysis for remote sensing,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 65, no. 1, pp. 2–16 , Jan. 2010
2010
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Dey, V., Zhang, Y., Zhong, and M., “A review on image segmentation techniques with remote sensing perspective,” Wagner, W., Székely, B. (Ed.), ISPRS TC VII Symposium – 100 Years ISPRS. Vienna, pp. 31–42. , 2010
2010
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Mountrakis and Giorgos., “Support vector machines in remote sensing: A review.” ISPRS Journal of Photogrammetry and Remote Sensing , 2011
2011
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Moser, G., Serpico, S.B., and B. J.A., “Land-cover mapping by markov modeling of spatial-contextual information in very-high-resolution remote sensing images,” Proc. IEEE 101, 631–651 , 2013
2013
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Sharma, Richa, Ghosh, Aniruddha, Joshi, and P.K., “Decision tree approach for classification of remotely sensed satellite data using open source support.” J. Earth Syst. Sci. 122 (5), 1237–1247. , 2013
2013
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Li, X., Myint, S.W., Zhang, Y., Galletti, C., Zhang, X., Turner, and B.L, “Object-based land-cover classification for metropolitan phoenix, arizona, using aerial photo- graphy,” Int. J. Appl. Earth Obs. Geoinf. 33, 321–330. , 2014
2014
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M. Belgiu, L. Drǎguţ, and J. Strobl, “Quantitative evaluation of variations in rule-based classifications of land cover in urban neighbourhoods using worldview-2 imagery,” ISPRS Journal of Photogrammetry and Remote Sensing , vol. 87, pp. 205–215, 2014
2014
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R. R. Colditz, “An evaluation of different training sample allocation schemes for discrete and continuous land cover classification using decision tree-based algorithms,” Remote Sensing , vol. 7, no. 8, pp. 9655–9681, 2015
2015
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A. Juel, G. B. Groom, J.-C. Svenning, and R. Ejrnaes, “Spatial application of random forest models for fine-scale coastal vegetation classification using object based analysis of aerial orthophoto and dem data,” International Journal of Applied Earth Observation and Geoinformation , vol. 42, pp. 106–114, 2015
2015
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A. Mellor, S. Boukir, A. Haywood, and S. Jones, “Exploring issues of training data imbalance and mislabelling on random forest performance for large area land cover classification using the ensemble margin,” ISPRS Journal of Photogrammetry and Remote Sensing , vol. 105, pp. 155–168, 2015
2015
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Ronneberger, O., Fischer, P., and Brox., “U-net: Convolutional networks for biomedical image segmentation.” International conference on medical image computing and computer-assisted intervention. pp. 234–241. Springer. , 2015
2015
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Zhang, Liangpei, Zhang, Lefei, and D. Bo, “Deep learning for remote sensing data: A technical tutorial on the state of the art,” IEEE Geoscience and Remote Sensing Magazine , vol. 4, no. 2, pp. 22–40, 2016
2016
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W. Wang, N. Yang, Y. Zhang, F. Wang, T. Cao, and P. Eklund, “A review of road extraction from remote sensing images,” Journal of Traffic and Transportation Engineering (English Edition), vol. 3, no. 3, pp. 271–282 , Jun. 2016
2016
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M. Belgiu and L. Drăguţ, “Random forest in remote sensing: A review of applications and future directions.” ISPRS Journal of Photogrammetry and Remote Sensing. , 2016
2016
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Zhang, L., Du, and B., “Deep learning for remote sensing data: A technical tutorial on the state of the art.” IEEE Geoscience and Remote Sensing Magazine, 4,22–40. , 2016
2016
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B. Zhou, A. Khosla, A. Lapedriza, A. Oliva, and A. Torralba, “Learning deep features for discriminative localization,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 2921–2929
2016
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Ball, J. E., Anderson, D. T., Chan, and C. S., “Comprehensive survey of deep learning in remote sensing: theories, tools, and challenges for the community,” Journal of Applied Remote Sensing, 11, 42609. , 2017
2017
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E. Maggiori, Y. Tarabalka, G. Charpiat, and P. Alliez, “Can semantic labeling methods generalize to any city? the inria aerial image labeling benchmark,” in IEEE International Geoscience and Remote Sensing Symposium (IGARSS) . IEEE, 2017
2017
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Huang, Bo, Zhao, Bei, Song, and Yimeng, “Urban land-use mapping using a deep convolutional neural network with high spatial resolution multispectral remote sensing imagery.” Remote Sens. Environ. 214 (September), 73–86. , 2018
2018
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Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, and A. L., “Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs.” IEEE Transactions on Pattern Analysis and Machine Intelligence,40, 834–848 , 2018
2018
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Z. Huang, X. Wang, J. Wang, W. Liu, and J. Wang, “Weakly-supervised semantic segmentation network with deep seeded region growing,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 7014–7023
2018
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K. Fu, W. Lu, W. Diao, M. Yan, H. Sun, Y. Zhang, and X. Sun, “Wsf-net: Weakly supervised feature-fusion network for binary segmentation in remote sensing image,” Remote Sensing , vol. 10, no. 12, p. 1970, 2018
2018
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I. Demir, K. Koperski, D. Lindenbaum, G. Pang, J. Huang, S. Basu, F. Hughes, D. Tuia, and R. Raskar, “Deepglobe 2018: A challenge to parse the earth through satellite images,” in The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops , June 2018
X. Yao, Q. Cao, X. Feng, G. Cheng, and J. Han, “Scale-aware detailed matching for few-shot aerial image semantic segmentation,” IEEE Transactions on Geoscience and Remote Sensing , vol. 60, pp. 1–11, 2021
2021
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G. Cheng, L. Cai, C. Lang, X. Yao, J. Chen, L. Guo, and J. Han, “Spnet: Siamese-prototype network for few-shot remote sensing image scene classification,” IEEE Transactions on Geoscience and Remote Sensing , vol. 60, pp. 1–11, 2021
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X. Li, D. Shi, X. Diao, and H. Xu, “Scl-mlnet: Boosting few-shot remote sensing scene classification via self-supervised contrastive learning,” IEEE Transactions on Geoscience and Remote Sensing , vol. 60, pp. 1–12, 2021
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Rasti, Behnood, Chang, Yi, Dalsasso, Emanuele, Denis, Loic, Ghamisi, and Pedram, “Image restoration for remote sensing: Overview and toolbox,” IEEE Geoscience and Remote Sensing Magazine , vol. 10, no. 2, pp. 201–230, 2022
2022
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2018
Cited alongside, same era.
M. D. Hossain and D. Chen, “Segmentation for object-based image analysis (obia): A review of algorithms and challenges from remote sensing perspective,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 150, pp. 115–134 , Apr. 2019
2019
Cited alongside, same era.
J. Ahn, S. Cho, and S. Kwak, “Weakly supervised learning of instance segmentation with inter-pixel relations,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2019, pp. 2209–2218
2019
Cited alongside, same era.
S. Mohajerani and P. Saeedi, “Cloud-net: An end-to-end cloud detection algorithm for landsat 8 imagery,” in IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium , 2019, pp. 1029–1032
2019
Cited alongside, same era.
Diakogiannis, F. I., Waldner, François, Caccetta, Peter, Chen, and Wu., “Resunet-a: A deep learning framework for semantic segmentation of remotely sensed data.” ISPRS J. Photogramm. Remote Sens. 162 (April), 94–114. , 2020
2020
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M. Toldo, A. Maracani, U. Michieli, and P. Zanuttigh, “Unsupervised domain adaptation in semantic segmentation: A review,” Technologies, vol. 8, no. 2, p. 35 , Jun. 2020
2020
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Li, Wenmei, W. Ziteng, Wang, Yu, Wu, Jiaqi, Wang, Juan, Jia, Yan, Gui, and Guan, “Classification of high-spatial-resolution remote sensing scenes method using transfer learning and deep convolutional neural network,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , vol. 13, pp. 1986–1995, 2020
2020
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P. de Lima, R., and M. K., “Convolutional neural network for remote-sensing scene classification: Transfer learning analysis,” Remote Sens. 2020, 12, 86. , 2020
2020
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T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, S. Agarwal, A. Herbert-Voss, G. Krueger, T. Henighan, R. Child, A. Ramesh, D. Ziegler, J. Wu, C. Winter, C. Hesse, M. Chen, E. Sigler, M. Litwin, S. Gray, B. jamin Chess, J. Clark, C. Berner, S. McCandlish, A. Radford, I. Sutskever, , and D. Amodei., “Language models are few-shot learners,” NeurIPS 2020 , 2020
2020
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G.-J. Qi and J. Luo, “Small data challenges in big data era: A survey of recent progress on unsupervised and semi-supervised methods,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 44, no. 4, pp. 2168–2187 , Apr. 2022
2022
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L. Mengxi, S. Qian, C. Zhuoqun, and L. Jianlong, “Pa-former: Learning prior-aware transformer for remote sensing building change detection,” IEEE Geoscience and Remote Sensing Letters , vol. 19, pp. 1–5, 2022
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Q. Zeng and J. Geng, “Task-specific contrastive learning for few-shot remote sensing image scene classification,” ISPRS Journal of Photogrammetry and Remote Sensing,vol191, Pages 143-154 , Sept 2022
2022
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S. Liu, L. Liu, F. Xu, J. Chen, Y. Yuan, and X. Chen, “A deep learning method for individual arable field (iaf) extraction with cross-domain adversarial capability,” Computers and Electronics in Agriculture , vol. 203, p. 107473, 2022
2022
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Z. Dong, S. An, J. Zhang, J. Yu, J. Li, and D. Xu, “L-unet: A landslide extraction model using multi-scale feature fusion and attention mechanism,” Remote Sensing , vol. 14, no. 11, p. 2552, 2022
2022
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Y. Xu and P. Ghamisi, “Consistency-regularized region-growing network for semantic segmentation of urban scenes with point-level annotations,” IEEE Transactions on Image Processing , vol. 31, pp. 5038–5051, 2022
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Y. G. G. S. Y. Z. J. Chen, J. Zhu and M. Deng., “Unsupervised domain adaptation for semantic segmentation of high-resolution remote sensing imagery driven by category-certainty attention.” IEEE Trans. Geosci. Remote Sens., vol. 60 , 2022
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R. Wenqi, T. Yang, S. Qiyu, Z. Chaoqiang, and H. Qing-Long, “Visual semantic segmentation based on few/zero-shot learning: An overview,” IEEE/CAA Journal of Automatica Sinica , pp. 1–21, 2023
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Z. Chang, Y. Lu, X. Ran, X. Gao, and X. Wangg, “Few-shot semantic segmentation: a review on recent approaches,” Neural Comput and Applic, vol. 35, pp. 18251–18275 , Sept 2023
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A. K. et al., “Segment anything,” arXiv, Apr. 05, 2023 , May 2023
2023
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G. Z. H. C. X. L. S. H. J. M. Z. L. H. L. Chen, Yujia and H. Wang., “A novel weakly supervised semantic segmentation framework to improve the resolution of land cover product.” ISPRS Journal of Photogrammetry and Remote Sensing. , 2023
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X. Z. Z. P. X. J. X. T. Wang, Guanchun and L. Jiao., “Mol: Towards accurate weakly supervised remote sensing object detection via multi-view noisy learning.” ISPRS Journal of Photogrammetry and Remote Sensing 196. , 2023
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Y. G. G. S. L. Y. M. D. Zhu, Jingru and J. Chen., “Unsupervised domain adaptation semantic segmentation of high-resolution remote sensing imagery with invariant domain-level prototype memory.” IEEE Transactions on Geoscience and Remote Sensing 61. , 2023
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W. Liu, X. Shen, C.-M. Pun, and X. Cun, “Explicit visual prompting for low-level structure segmentations,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 19 434–19 445
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