| Convolutional neural networks for passive monitoring of a shallow water environment using a single sensor EL Ferguson, R Ramakrishnan, SB Williams, CT Jin Acoustics, Speech and Signal Processing (ICASSP), 2017 IEEE International …, 2017 | 68 | 2017 |
| Pretraining for Hyperspectral Convolutional Neural Network Classification L Windrim, A Melkumyan, RJ Murphy, A Chlingaryan, R Ramakrishnan IEEE Transactions on Geoscience and Remote Sensing, 2018 | 65 | 2018 |
| A Physics-Based Deep Learning Approach to Shadow Invariant Representations of Hyperspectral Images L Windrim, R Ramakrishnan, A Melkumyan, RJ Murphy IEEE Transactions on Image Processing 27 (2), 665-677, 2018 | 45 | 2018 |
| Image processing and object classification TV Calleja, R Ramakrishnan US Patent 9,547,807, 2017 | 34 | 2017 |
| Hyperspectral CNN Classification with Limited Training Samples L Windrim, R Ramakrishnan, A Melkumyan, R Murphy arXiv preprint arXiv:1611.09007, 2016 | 22 | 2016 |
| Deep learning approach to passive monitoring of the underwater acoustic environment EL Ferguson, R Ramakrishnan, SB Williams, CT Jin The Journal of the Acoustical Society of America 140 (4), 3351-3351, 2016 | 19 | 2016 |
| Shadow compensation for outdoor perception R Ramakrishnan, J Nieto, S Scheding 2015 IEEE International Conference on Robotics and Automation (ICRA), 4835-4842, 2015 | 17 | 2015 |
| Combining strong features for registration of hyperspectral and LiDAR data from field-based platforms ST Monteiro, J Nieto, R Murphy, R Ramakrishnan, Z Taylor 2013 IEEE International Geoscience and Remote Sensing Symposium-IGARSS, 1210 …, 2013 | 8 | 2013 |
| Illumination Invariant Outdoor Perception R Ramakrishnan University of Sydney, 2015 | 2 | 2015 |
| Verification of Sky Models for Image Calibration R Ramakrishnan, J Nieto, S Scheding Proceedings of the 2013 IEEE International Conference on Computer Vision …, 2013 | 1 | 2013 |
