| Spatial-spectral transformer for hyperspectral image classification X He, Y Chen, Z Lin Remote Sensing 13 (3), 498, 2021 | 325 | 2021 |
| Automatic design of convolutional neural network for hyperspectral image classification Y Chen, K Zhu, L Zhu, X He, P Ghamisi, JA Benediktsson IEEE Transactions on Geoscience and Remote Sensing 57 (9), 7048-7066, 2019 | 194 | 2019 |
| Heterogeneous transfer learning for hyperspectral image classification based on convolutional neural network X He, Y Chen, P Ghamisi IEEE Transactions on Geoscience and Remote Sensing 58 (5), 3246-3263, 2019 | 169 | 2019 |
| Deep learning ensemble for hyperspectral image classification Y Chen, Y Wang, Y Gu, X He, P Ghamisi, X Jia IEEE Journal of Selected Topics in Applied Earth Observations and Remote …, 2019 | 162 | 2019 |
| Optimized input for CNN-based hyperspectral image classification using spatial transformer network X He, Y Chen IEEE Geoscience and Remote Sensing Letters 16 (12), 1884-1888, 2019 | 94 | 2019 |
| Transferring CNN ensemble for hyperspectral image classification X He, Y Chen IEEE Geoscience and Remote Sensing Letters 18 (5), 876-880, 2020 | 67 | 2020 |
| Spectral–spatial masked transformer with supervised and contrastive learning for hyperspectral image classification L Huang, Y Chen, X He IEEE Transactions on Geoscience and Remote Sensing 61, 1-18, 2023 | 63 | 2023 |
| Dual graph convolutional network for hyperspectral image classification with limited training samples X He, Y Chen, P Ghamisi IEEE Transactions on Geoscience and Remote Sensing 60, 1-18, 2021 | 61 | 2021 |
| Modifications of the multi-layer perceptron for hyperspectral image classification X He, Y Chen Remote Sensing 13 (17), 3547, 2021 | 48 | 2021 |
| LiDAR data classification using morphological profiles and convolutional neural networks A Wang, X He, P Ghamisi, Y Chen IEEE Geoscience and Remote Sensing Letters 15 (5), 774-778, 2018 | 38 | 2018 |
| LiDAR data classification using spatial transformation and CNN X He, A Wang, P Ghamisi, G Li, Y Chen IEEE Geoscience and Remote Sensing Letters 16 (1), 125-129, 2018 | 33 | 2018 |
| Spectral-spatial mamba for hyperspectral image classification L Huang, Y Chen, X He arXiv preprint arXiv:2404.18401, 2024 | 26 | 2024 |
| Soft augmentation-based Siamese CNN for hyperspectral image classification with limited training samples W Wang, Y Chen, X He, Z Li IEEE Geoscience and Remote Sensing Letters 19, 1-5, 2021 | 25 | 2021 |
| Foundation model-based multimodal remote sensing data classification X He, Y Chen, L Huang, D Hong, Q Du IEEE Transactions on Geoscience and Remote Sensing 62, 1-17, 2023 | 24 | 2023 |
| Two-branch pure transformer for hyperspectral image classification X He, Y Chen, Q Li IEEE Geoscience and Remote Sensing Letters 19, 1-5, 2022 | 16 | 2022 |
| Bayesian deep learning for hyperspectral image classification with low uncertainty X He, Y Chen, L Huang IEEE Transactions on Geoscience and Remote Sensing 61, 1-16, 2023 | 11 | 2023 |
| Co-training transformer for remote sensing image classification, segmentation, and detection Q Li, Y Chen, X He, L Huang IEEE Transactions on Geoscience and Remote Sensing 62, 1-18, 2024 | 10 | 2024 |
| Toward a trustworthy classifier with deep CNN: uncertainty estimation meets hyperspectral image X He, Y Chen, L Huang IEEE Transactions on Geoscience and Remote Sensing 60, 1-15, 2022 | 10 | 2022 |
| Weakly supervised classification of hyperspectral image based on complementary learning L Huang, Y Chen, X He Remote Sensing 13 (24), 5009, 2021 | 9 | 2021 |
| Supervised contrastive learning-based classification for hyperspectral image L Huang, Y Chen, X He, P Ghamisi Remote Sensing 14 (21), 5530, 2022 | 5 | 2022 |