| Hyperspectral unmixing overview: Geometrical, statistical, and sparse regression-based approaches JM Bioucas-Dias, A Plaza, N Dobigeon, M Parente, Q Du, P Gader, ... IEEE journal of selected topics in applied earth observations and remote …, 2012 | 3036 | 2012 |
| Estimation of number of spectrally distinct signal sources in hyperspectral imagery CI Chang, Q Du IEEE Transactions on geoscience and remote sensing 42 (3), 608-619, 2004 | 1224 | 2004 |
| More diverse means better: Multimodal deep learning meets remote-sensing imagery classification D Hong, L Gao, N Yokoya, J Yao, J Chanussot, Q Du, B Zhang IEEE Transactions on Geoscience and Remote Sensing 59 (5), 4340-4354, 2020 | 1099 | 2020 |
| Hyperspectral image classification using deep pixel-pair features W Li, G Wu, F Zhang, Q Du IEEE Transactions on Geoscience and Remote Sensing 55 (2), 844-853, 2016 | 824 | 2016 |
| Local binary patterns and extreme learning machine for hyperspectral imagery classification W Li, C Chen, H Su, Q Du IEEE Transactions on Geoscience and Remote Sensing 53 (7), 3681-3693, 2015 | 732 | 2015 |
| A joint band prioritization and band-decorrelation approach to band selection for hyperspectral image classification CI Chang, Q Du, TL Sun, MLG Althouse IEEE transactions on geoscience and remote sensing 37 (6), 2631-2641, 1999 | 706 | 1999 |
| Collaborative representation for hyperspectral anomaly detection W Li, Q Du IEEE Transactions on geoscience and remote sensing 53 (3), 1463-1474, 2014 | 652 | 2014 |
| An improved box-counting method for image fractal dimension estimation J Li, Q Du, C Sun Pattern recognition 42 (11), 2460-2469, 2009 | 652 | 2009 |
| Diverse region-based CNN for hyperspectral image classification M Zhang, W Li, Q Du IEEE Transactions on Image Processing 27 (6), 2623-2634, 2018 | 582 | 2018 |
| Multisource remote sensing data classification based on convolutional neural network X Xu, W Li, Q Ran, Q Du, L Gao, B Zhang IEEE Transactions on Geoscience and Remote Sensing 56 (2), 937-949, 2017 | 571 | 2017 |
| Hyperspectral image compression using JPEG2000 and principal component analysis Q Du, JE Fowler IEEE Geoscience and Remote sensing letters 4 (2), 201-205, 2007 | 562 | 2007 |
| GETNET: A general end-to-end 2-D CNN framework for hyperspectral image change detection Q Wang, Z Yuan, Q Du, X Li IEEE Transactions on Geoscience and Remote Sensing 57 (1), 3-13, 2018 | 519 | 2018 |
| Hyperspectral and LiDAR data fusion: Outcome of the 2013 GRSS data fusion contest C Debes, A Merentitis, R Heremans, J Hahn, N Frangiadakis, ... IEEE Journal of Selected Topics in Applied Earth Observations and Remote …, 2014 | 508 | 2014 |
| Similarity-based unsupervised band selection for hyperspectral image analysis Q Du, H Yang IEEE geoscience and remote sensing letters 5 (4), 564-568, 2008 | 473 | 2008 |
| Hyperspectral band selection: A review W Sun, Q Du IEEE Geoscience and Remote Sensing Magazine 7 (2), 118-139, 2019 | 398 | 2019 |
| Unsupervised spatial–spectral feature learning by 3D convolutional autoencoder for hyperspectral classification S Mei, J Ji, Y Geng, Z Zhang, X Li, Q Du IEEE Transactions on Geoscience and Remote Sensing 57 (9), 6808-6820, 2019 | 313 | 2019 |
| Learning sensor-specific spatial-spectral features of hyperspectral images via convolutional neural networks S Mei, J Ji, J Hou, X Li, Q Du IEEE Transactions on Geoscience and Remote Sensing 55 (8), 4520-4533, 2017 | 311 | 2017 |
| Transferred deep learning for anomaly detection in hyperspectral imagery W Li, G Wu, Q Du IEEE Geoscience and Remote Sensing Letters 14 (5), 597-601, 2017 | 304 | 2017 |
| An efficient method for supervised hyperspectral band selection H Yang, Q Du, H Su, Y Sheng IEEE Geoscience and Remote Sensing Letters 8 (1), 138-142, 2010 | 295 | 2010 |
| Hyperspectral image spatial super-resolution via 3D full convolutional neural network S Mei, X Yuan, J Ji, Y Zhang, S Wan, Q Du Remote Sensing 9 (11), 1139, 2017 | 284 | 2017 |