| 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 | 1679 | 2017 |
| Deep recurrent neural networks for hyperspectral image classification L Mou, P Ghamisi, XX Zhu IEEE Transactions on Geoscience and Remote Sensing 55 (7), 3639-3655, 2017 | 724 | 2017 |
| Tomographic SAR inversion by L1-norm regularization—The compressive sensing approach XX Zhu, R Bamler Geoscience and Remote Sensing, IEEE Transactions on 48 (10), 3839-3846, 2010 | 489 | 2010 |
| Very high resolution spaceborne SAR tomography in urban environment XX Zhu, R Bamler IEEE Transactions on Geoscience and Remote Sensing 48 (12), 4296-4308, 2010 | 432 | 2010 |
| An augmented linear mixing model to address spectral variability for hyperspectral unmixing D Hong, N Yokoya, J Chanussot, XX Zhu IEEE Transactions on Image Processing 28 (4), 1923-1938, 2018 | 384 | 2018 |
| A sparse image fusion algorithm with application to pan-sharpening XX Zhu, R Bamler IEEE transactions on geoscience and remote sensing 51 (5), 2827-2836, 2012 | 382 | 2012 |
| Super-resolution power and robustness of compressive sensing for spectral estimation with application to spaceborne tomographic SAR XX Zhu, R Bamler IEEE Transactions on Geoscience and Remote Sensing 50 (1), 247-258, 2011 | 304 | 2011 |
| Learning spectral-spatial-temporal features via a recurrent convolutional neural network for change detection in multispectral imagery L Mou, L Bruzzone, XX Zhu IEEE Transactions on Geoscience and Remote Sensing 57 (2), 924-935, 2019 | 273 | 2019 |
| Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans M Roberts, D Driggs, M Thorpe, J Gilbey, M Yeung, S Ursprung, ... Nature Machine Intelligence, 2021 | 267 | 2021 |
| Unsupervised spectral–spatial feature learning via deep residual Conv–Deconv network for hyperspectral image classification L Mou, P Ghamisi, XX Zhu IEEE Transactions on Geoscience and Remote Sensing 56 (1), 391-406, 2017 | 197 | 2017 |
| Building instance classification using street view images J Kang, M Körner, Y Wang, H Taubenböck, XX Zhu ISPRS journal of photogrammetry and remote sensing 145, 44-59, 2018 | 185 | 2018 |
| Data fusion and remote sensing: An ever-growing relationship M Schmitt, XX Zhu IEEE Geoscience and Remote Sensing Magazine 4 (4), 6-23, 2016 | 173 | 2016 |
| Semantic segmentation of slums in satellite images using transfer learning on fully convolutional neural networks M Wurm, T Stark, XX Zhu, M Weigand, H Taubenböck ISPRS journal of photogrammetry and remote sensing 150, 59-69, 2019 | 171 | 2019 |
| Linking points with labels in 3D: A review of point cloud semantic segmentation Y Xie, J Tian, XX Zhu IEEE Geoscience and Remote Sensing Magazine 8 (4), 38-59, 2020 | 168 | 2020 |
| Learnable manifold alignment (LeMA): A semi-supervised cross-modality learning framework for land cover and land use classification D Hong, N Yokoya, N Ge, J Chanussot, XX Zhu ISPRS journal of photogrammetry and remote sensing 147, 193-205, 2019 | 162 | 2019 |
| Demonstration of super-resolution for tomographic SAR imaging in urban environment XX Zhu, R Bamler IEEE Transactions on Geoscience and Remote Sensing 50 (8), 3150-3157, 2011 | 151 | 2011 |
| Invariant attribute profiles: A spatial-frequency joint feature extractor for hyperspectral image classification D Hong, X Wu, P Ghamisi, J Chanussot, N Yokoya, XX Zhu IEEE Transactions on Geoscience and Remote Sensing 58 (6), 3791-3808, 2020 | 141 | 2020 |
| CoSpace: Common subspace learning from hyperspectral-multispectral correspondences D Hong, N Yokoya, J Chanussot, XX Zhu IEEE Transactions on Geoscience and Remote Sensing 57 (7), 4349-4359, 2019 | 140 | 2019 |
| Hyperspectral and LiDAR data fusion using extinction profiles and deep convolutional neural network P Ghamisi, B Höfle, XX Zhu IEEE Journal of Selected Topics in Applied Earth Observations and Remote …, 2016 | 140 | 2016 |
| A self-improving convolution neural network for the classification of hyperspectral data P Ghamisi, Y Chen, XX Zhu IEEE Geoscience and Remote Sensing Letters 13 (10), 1537-1541, 2016 | 127 | 2016 |
Lichao MouGerman Aerospace Center (DLR), Technical University of Munich (TUM)Verified email at dlr.de
