| DDcGAN: A dual-discriminator conditional generative adversarial network for multi-resolution image fusion J Ma, H Xu, J Jiang, X Mei, XP Zhang IEEE Transactions on Image Processing 29, 4980-4995, 2020 | 764 | 2020 |
| SwinFusion: Cross-domain long-range learning for general image fusion via swin transformer J Ma, L Tang, F Fan, J Huang, X Mei, Y Ma IEEE/CAA Journal of Automatica Sinica 9 (7), 1200-1217, 2022 | 432 | 2022 |
| Infrared and visible image fusion based on target-enhanced multiscale transform decomposition J Chen, X Li, L Luo, X Mei, J Ma Information Sciences 508, 64-78, 2020 | 297 | 2020 |
| Spectral-spatial attention networks for hyperspectral image classification X Mei, E Pan, Y Ma, X Dai, J Huang, F Fan, Q Du, H Zheng, J Ma Remote Sensing 11 (8), 963, 2019 | 245 | 2019 |
| Hyperspectral image denoising with superpixel segmentation and low-rank representation F Fan, Y Ma, C Li, X Mei, J Huang, J Ma Information Sciences 397, 48-68, 2017 | 139 | 2017 |
| An infrared small target detecting algorithm based on human visual system J Han, Y Ma, J Huang, X Mei, J Ma IEEE Geoscience and Remote Sensing Letters 13 (3), 452-456, 2016 | 135 | 2016 |
| Hyperspectral anomaly detection with robust graph autoencoders G Fan, Y Ma, X Mei, F Fan, J Huang, J Ma IEEE Transactions on Geoscience and Remote Sensing 60, 1-14, 2021 | 103 | 2021 |
| Hyperspectral image classification with robust sparse representation C Li, Y Ma, X Mei, C Liu, J Ma IEEE Geoscience and Remote Sensing Letters 13 (5), 641-645, 2016 | 99 | 2016 |
| Hyperspectral image denoising using the robust low-rank tensor recovery C Li, Y Ma, J Huang, X Mei, J Ma JOSA A 32 (9), 1604-1612, 2015 | 82 | 2015 |
| Robust Sparse Hyperspectral Unmixing With Norm Y Ma, C Li, X Mei, C Liu, J Ma IEEE Transactions on Geoscience and Remote Sensing 55 (3), 1227-1239, 2017 | 77 | 2017 |
| Spectral-spatial classification for hyperspectral image based on a single GRU E Pan, X Mei, Q Wang, Y Ma, J Ma Neurocomputing 387, 150-160, 2020 | 58 | 2020 |
| Hyperspectral anomaly detection via integration of feature extraction and background purification Y Ma, G Fan, Q Jin, J Huang, X Mei, J Ma IEEE Geoscience and Remote Sensing Letters 18 (8), 1436-1440, 2020 | 51 | 2020 |
| TANet: An Unsupervised Two-Stream Autoencoder Network for Hyperspectral Unmixing Q Jin, Y Ma, X Mei, J Ma IEEE Transactions on Geoscience and Remote Sensing 60, 1-15, 2021 | 49 | 2021 |
| Adversarial autoencoder network for hyperspectral unmixing Q Jin, Y Ma, F Fan, J Huang, X Mei, J Ma IEEE Transactions on Neural Networks and Learning Systems, 2021 | 44 | 2021 |
| Robust GBM hyperspectral image unmixing with superpixel segmentation based low rank and sparse representation X Mei, Y Ma, C Li, F Fan, J Huang, J Ma Neurocomputing 275, 2783-2797, 2018 | 44 | 2018 |
| Hyperspectral unmixing with robust collaborative sparse regression C Li, Y Ma, X Mei, C Liu, J Ma Remote Sensing 8 (7), 588, 2016 | 42 | 2016 |
| Locality-constrained sparse representation for hyperspectral image classification Y Zhang, Y Ma, X Dai, H Li, X Mei, J Ma Information Sciences 546, 858-870, 2021 | 35 | 2021 |
| A generative adversarial network with adaptive constraints for multi-focus image fusion J Huang, Z Le, Y Ma, X Mei, F Fan Neural Computing and Applications 32, 15119-15129, 2020 | 34 | 2020 |
| Infrared and visible image fusion based on total variation and augmented Lagrangian H Guo, Y Ma, X Mei, J Ma JOSA A 34 (11), 1961-1968, 2017 | 33 | 2017 |
| GBM-based unmixing of hyperspectral data using bound projected optimal gradient method C Li, Y Ma, J Huang, X Mei, C Liu, J Ma IEEE Geoscience and Remote Sensing Letters 13 (7), 952-956, 2016 | 33 | 2016 |
