| Deep learning in remote sensing: A comprehensive review and list of resources XX Zhu, D Tuia, L Mou, GS Xia, L Zhang, F Xu, F Fraundorfer IEEE geoscience and remote sensing magazine 5 (4), 8-36, 2017 | 2976 | 2017 |
| DOTA: A large-scale dataset for object detection in aerial images GS Xia, X Bai, J Ding, Z Zhu, S Belongie, J Luo, M Datcu, M Pelillo, ... Proceedings of the IEEE conference on computer vision and pattern …, 2018 | 2675 | 2018 |
| Deep learning for remote sensing data: A technical tutorial on the state of the art L Zhang, L Zhang, B Du IEEE Geoscience and remote sensing magazine 4 (2), 22-40, 2016 | 2154 | 2016 |
| AID: A benchmark data set for performance evaluation of aerial scene classification GS Xia, J Hu, F Hu, B Shi, X Bai, Y Zhong, L Zhang, X Lu IEEE Transactions on Geoscience and Remote Sensing 55 (7), 3965-3981, 2017 | 1897 | 2017 |
| Transferring deep convolutional neural networks for the scene classification of high-resolution remote sensing imagery F Hu, GS Xia, J Hu, L Zhang Remote Sensing 7 (11), 14680-14707, 2015 | 1316 | 2015 |
| Deep learning in environmental remote sensing: Achievements and challenges Q Yuan, H Shen, T Li, Z Li, S Li, Y Jiang, H Xu, W Tan, Q Yang, J Wang, ... Remote sensing of Environment 241, 111716, 2020 | 1141 | 2020 |
| Hyperspectral image restoration using low-rank matrix recovery H Zhang, W He, L Zhang, H Shen, Q Yuan IEEE transactions on geoscience and remote sensing 52 (8), 4729-4743, 2013 | 844 | 2013 |
| Land-cover classification with high-resolution remote sensing images using transferable deep models XY Tong, GS Xia, Q Lu, H Shen, S Li, S You, L Zhang Remote Sensing of Environment 237, 111322, 2020 | 744 | 2020 |
| Saliency-guided unsupervised feature learning for scene classification F Zhang, B Du, L Zhang IEEE transactions on Geoscience and Remote Sensing 53 (4), 2175-2184, 2014 | 614 | 2014 |
| Total-variation-regularized low-rank matrix factorization for hyperspectral image restoration W He, H Zhang, L Zhang, H Shen IEEE transactions on geoscience and remote sensing 54 (1), 178-188, 2015 | 612 | 2015 |
| Hyperspectral image denoising employing a spectral–spatial adaptive total variation model Q Yuan, L Zhang, H Shen IEEE Transactions on Geoscience and Remote Sensing 50 (10), 3660-3677, 2012 | 603 | 2012 |
| A deeply supervised attention metric-based network and an open aerial image dataset for remote sensing change detection Q Shi, M Liu, S Li, X Liu, F Wang, L Zhang IEEE transactions on geoscience and remote sensing 60, 1-16, 2021 | 559 | 2021 |
| Image super-resolution: The techniques, applications, and future L Yue, H Shen, J Li, Q Yuan, H Zhang, L Zhang Signal processing 128, 389-408, 2016 | 557 | 2016 |
| A multiscale and multidepth convolutional neural network for remote sensing imagery pan-sharpening Q Yuan, Y Wei, X Meng, H Shen, L Zhang IEEE Journal of Selected Topics in Applied Earth Observations and Remote …, 2018 | 518 | 2018 |
| An SVM ensemble approach combining spectral, structural, and semantic features for the classification of high-resolution remotely sensed imagery X Huang, L Zhang IEEE transactions on geoscience and remote sensing 51 (1), 257-272, 2012 | 503 | 2012 |
| Missing information reconstruction of remote sensing data: A technical review H Shen, X Li, Q Cheng, C Zeng, G Yang, H Li, L Zhang IEEE Geoscience and Remote Sensing Magazine 3 (3), 61-85, 2015 | 484 | 2015 |
| 高光谱遥感 张良培 武汉大学出版社, 2005 | 476* | 2005 |
| Scene classification via a gradient boosting random convolutional network framework F Zhang, B Du, L Zhang IEEE Transactions on geoscience and remote sensing 54 (3), 1793-1802, 2015 | 468 | 2015 |
| Boosting the accuracy of multispectral image pansharpening by learning a deep residual network Y Wei, Q Yuan, H Shen, L Zhang IEEE Geoscience and Remote Sensing Letters 14 (10), 1795-1799, 2017 | 458 | 2017 |
| Stacked convolutional denoising auto-encoders for feature representation B Du, W Xiong, J Wu, L Zhang, L Zhang, D Tao IEEE transactions on cybernetics 47 (4), 1017-1027, 2016 | 452 | 2016 |
Huanfeng ShenProfessor, School of Resource and Environmental Science, Wuhan UniversityEmail được xác minh tại whu.edu.cn
