| A survey: Deep learning for hyperspectral image classification with few labeled samples S Jia, S Jiang, Z Lin, N Li, M Xu, S Yu Neurocomputing 448, 179-204, 2021 | 251 | 2021 |
| Hyperspectral anomaly detection via density peak clustering B Tu, X Yang, N Li, C Zhou, D He Pattern Recognition Letters 129, 144-149, 2020 | 63 | 2020 |
| Hyperspectral image classification with multi-scale feature extraction B Tu, N Li, L Fang, D He, P Ghamisi Remote Sensing 11 (5), 534, 2019 | 31 | 2019 |
| Hyperspectral anomaly detection via spatial density background purification B Tu, N Li, Z Liao, X Ou, G Zhang Remote Sensing 11 (22), 2618, 2019 | 27 | 2019 |
| Hyperspectral image classification via superpixel correlation coefficient representation B Tu, X Yang, N Li, X Ou, W He IEEE Journal of Selected Topics in Applied Earth Observations and Remote …, 2018 | 22 | 2018 |
| A multiscale superpixel-level group clustering framework for hyperspectral band selection S Jia, Y Yuan, N Li, J Liao, Q Huang, X Jia, M Xu IEEE Transactions on Geoscience and Remote Sensing 60, 1-18, 2022 | 20 | 2022 |
| Hyperspectral anomaly detection via dual collaborative representation G Zhang, N Li, B Tu, Z Liao, Y Peng IEEE Journal of Selected Topics in Applied Earth Observations and Remote …, 2020 | 19 | 2020 |
| Spatial-spectral classification of hyperspectral image via group tensor decomposition G Zhao, B Tu, H Fei, N Li, X Yang Neurocomputing 316, 68-77, 2018 | 19 | 2018 |
| Hyperspectral image classification with a class-dependent spatial–spectral mixed metric B Tu, N Li, L Fang, X Yang, J Wu Pattern Recognition Letters 123, 16-22, 2019 | 9 | 2019 |
| Density peak covariance matrix for feature extraction of hyperspectral image G Zhao, N Li, B Tu, G Zhang, W He IEEE Geoscience and Remote Sensing Letters 17 (3), 534-538, 2019 | 8 | 2019 |
| Classification of hyperspectral images via weighted spatial correlation representation B Tu, N Li, L Fang, H Fei, D He Journal of Visual Communication and Image Representation 56, 160-166, 2018 | 7 | 2018 |
| Superpixel-guided variable Gabor phase coding fusion for hyperspectral image classification S Zhang, D Tang, N Li, X Jia, S Jia IEEE Transactions on Geoscience and Remote Sensing 60, 1-16, 2022 | 5 | 2022 |
| Structure-aware multikernel learning for hyperspectral image classification C Zhou, B Tu, N Li, W He, A Plaza IEEE Journal of Selected Topics in Applied Earth Observations and Remote …, 2021 | 5 | 2021 |
| Spectral context-aware transformer for cholangiocarcinoma hyperspectral image segmentation N Li, J Xue, S Jia Proceedings of the 2022 5th International Conference on Image and Graphics …, 2022 | 4 | 2022 |
| Density peak based covariance matrix for hyperspectral images classification B Tu, N Li, W Kuang, J Wang, C Zhou IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium …, 2019 | 2 | 2019 |
| Texture-Aware Self-Attention Model for Hyperspectral Tree Species Classification N Li, S Jiang, J Xue, S Ye, S Jia IEEE Transactions on Geoscience and Remote Sensing, 2023 | 1 | 2023 |
| Principal component self-attention mechanism for melanoma hyperspectral image recognition H Liang, N Li, J Xue, Y Long, S Jia Proceedings of the 2022 11th International Conference on Computing and …, 2022 | | 2022 |
| HSI-DETR: A DETR-based Transfer Learning from RGB to Hyperspectral Images for Object Detection of Live and Dead Cells: To achieve better results, convert models with the fewest … S Ye, N Li, J Xue, Y Long, S Jia Proceedings of the 2022 11th International Conference on Computing and …, 2022 | | 2022 |
| Superpixel Regularized Multiple kernel Gabor Fusion for Hyperspectral Image Classification D Tang, S Jia, N Li Proceedings of the 2021 10th International Conference on Computing and …, 2021 | | 2021 |
Bing TuIEEE Senior member, Nanjing University of Information Science and TechnologyZweryfikowany adres z nuist.edu.cn
