| SEN12MS--A curated dataset of georeferenced multi-spectral sentinel-1/2 imagery for deep learning and data fusion M Schmitt, LH Hughes, C Qiu, XX Zhu arXiv preprint arXiv:1906.07789, 2019 | 306 | 2019 |
| The SEN1-2 dataset for deep learning in SAR-optical data fusion M Schmitt, LH Hughes, XX Zhu arXiv preprint arXiv:1807.01569, 2018 | 262 | 2018 |
| Identifying corresponding patches in SAR and optical images with a pseudo-siamese CNN LH Hughes, M Schmitt, L Mou, Y Wang, XX Zhu IEEE Geoscience and Remote Sensing Letters 15 (5), 784-788, 2018 | 229 | 2018 |
| So2Sat LCZ42: A benchmark data set for the classification of global local climate zones [Software and Data Sets] XX Zhu, J Hu, C Qiu, Y Shi, J Kang, L Mou, H Bagheri, M Haberle, Y Hua, ... IEEE Geoscience and Remote Sensing Magazine 8 (3), 76-89, 2020 | 121 | 2020 |
| A deep learning framework for matching of SAR and optical imagery LH Hughes, D Marcos, S Lobry, D Tuia, M Schmitt ISPRS Journal of Photogrammetry and Remote Sensing 169, 166-179, 2020 | 118 | 2020 |
| So2Sat LCZ42: A benchmark dataset for global local climate zones classification XX Zhu, J Hu, C Qiu, Y Shi, J Kang, L Mou, H Bagheri, M Häberle, Y Hua, ... arXiv preprint arXiv:1912.12171, 2019 | 80 | 2019 |
| Aggregating cloud-free sentinel-2 images with google earth engine M Schmitt, LH Hughes, C Qiu, XX Zhu ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information …, 2019 | 69 | 2019 |
| Mining hard negative samples for SAR-optical image matching using generative adversarial networks LH Hughes, M Schmitt, XX Zhu Remote Sensing 10 (10), 1552, 2018 | 57 | 2018 |
| Deep learning for SAR-optical image matching LH Hughes, N Merkle, T Bürgmann, S Auer, M Schmitt IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium …, 2019 | 38 | 2019 |
| SEN12MS—A Curated Dataset of Georeferenced Multi-Spectral Sentinel-1/2 Imagery for Deep Learning and Data Fusion. arXiv 2019 M Schmitt, LH Hughes, C Qiu, XX Zhu arXiv preprint arXiv:1906.07789, 0 | 33 | |
| Colorizing sentinel-1 sar images using a variational autoencoder conditioned on sentinel-2 imagery M Schmitt, LH Hughes, M Körner, XX Zhu The International Archives of the Photogrammetry, Remote Sensing and Spatial …, 2018 | 27 | 2018 |
| The SEN1-2 dataset for deep learning in SAR-optical data fusion. arXiv 2018 M Schmitt, LH Hughes, XX Zhu arXiv preprint arXiv:1807.01569, 0 | 23 | |
| Multitask learning for human settlement extent regression and local climate zone classification C Qiu, L Liebel, LH Hughes, M Schmitt, M Körner, XX Zhu IEEE Geoscience and Remote Sensing Letters 19, 1-5, 2020 | 17 | 2020 |
| A semi-supervised approach to SAR-optical image matching LH Hughes, M Schmitt ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information …, 2019 | 13 | 2019 |
| 2020 ieee grss data fusion contest M Schmitt, L Hughes, P Ghamisi, N Yokoya, R Hansch (No Title), 2019 | 9 | 2019 |
| Generative Adversarial Networks for Hard Negative Mining in CNN-Based SAR-Optical Image Matching LH Hughes, M Schmitt, XX Zhu IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium …, 2018 | 6 | 2018 |
| A Cluster Graph Approach to Land Cover Classification Boosting LH Hughes, S Streicher, E Chuprikova, J Du Preez Data 4 (1), 2019 | 5 | 2019 |
| Enhancing mobile camera pose estimation through the inclusion of sensors LH Hughes Stellenbosch: Stellenbosch University, 2014 | 5 | 2014 |
| On the selection and effectiveness of pseudo-absences for species distribution modeling with deep learning R Zbinden, N Van Tiel, B Kellenberger, L Hughes, D Tuia Ecological Informatics 81, 102623, 2024 | 4 | 2024 |
| Deep learning for matching high-resolution SAR and optical imagery LH Hughes Technische Universität München, 2020 | 4 | 2020 |
Michael SchmittBundeswehr University MunichVerified email at unibw.de
