| Deep count: fruit counting based on deep simulated learning M Rahnemoonfar, C Sheppard Sensors 17 (4), 905, 2017 | 532 | 2017 |
| Floodnet: A high resolution aerial imagery dataset for post flood scene understanding M Rahnemoonfar, T Chowdhury, A Sarkar, D Varshney, M Yari, ... IEEE Access 9, 89644-89654, 2021 | 164 | 2021 |
| Flooded area detection from UAV images based on densely connected recurrent neural networks M Rahnemoonfar, R Murphy, MV Miquel, D Dobbs, A Adams IEEE IGARSS 2018, 2018 | 71 | 2018 |
| Real-time yield estimation based on deep learning M Rahnemoonfar, C Sheppard Autonomous Air and Ground Sensing Systems for Agricultural Optimization and …, 2017 | 40 | 2017 |
| Comprehensive semantic segmentation on high resolution uav imagery for natural disaster damage assessment T Chowdhury, M Rahnemoonfar, R Murphy, O Fernandes 2020 IEEE International Conference on Big Data (Big Data), 3904-3913, 2020 | 35 | 2020 |
| DisCountNet: Discriminating and counting network for real-time counting and localization of sparse objects in high-resolution UAV imagery M Rahnemoonfar, D Dobbs, M Yari, MJ Starek Remote Sensing 11 (9), 1128, 2019 | 35 | 2019 |
| Automatic ice surface and bottom boundaries estimation in radar imagery based on level-set approach M Rahnemoonfar, GC Fox, M Yari, J Paden IEEE Transactions on Geoscience and Remote Sensing 55 (9), 5115-5122, 2017 | 34 | 2017 |
| Real-time scene understanding for UAV imagery based on deep convolutional neural networks C Sheppard, M Rahnemoonfar 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS …, 2017 | 33 | 2017 |
| Performance evaluation of object-based and pixel-based building detection algorithms from very high spatial resolution imagery I Khosravi, M Momeni, M Rahnemoonfar Photogrammetric Engineering & Remote Sensing 80 (6), 519-528, 2014 | 29 | 2014 |
| Deep multi-scale learning for automatic tracking of internal layers of ice in radar data M Rahnemoonfar, M Yari, J Paden, L Koenig, O Ibikunle Journal of Glaciology 67 (261), 39-48, 2021 | 27 | 2021 |
| SEMANTIC SEGMENTATION OF UNDERWATER SONAR IMAGERY WITH DEEP LEARNING M Rahnemoonfar, D Dobbs IGARSS 2019, 2019 | 26 | 2019 |
| Automatic seagrass disturbance pattern identification on sonar images M Rahnemoonfar, AF Rahman, RJ Kline, A Greene IEEE Journal of Oceanic Engineering 44 (1), 132-141, 2018 | 24 | 2018 |
| RescueNet: A high resolution UAV semantic segmentation benchmark dataset for natural disaster damage assessment T Chowdhury, R Murphy, M Rahnemoonfar arXiv preprint arXiv:2202.12361, 2022 | 23 | 2022 |
| Deep hybrid wavelet network for ice boundary detection in RADAR imagery H Kamangir, M Rahnemoonfar, D Dobbs, J Paden, G Fox IEEE IGARSS, 2018 | 23 | 2018 |
| Ai radar sensor: Creating radar depth sounder images based on generative adversarial network M Rahnemoonfar, J Johnson, J Paden Sensors 19 (24), 5479, 2019 | 20 | 2019 |
| Smart tracking of internal layers of ice in radar data via multi-scale learning M Yari, M Rahnemoonfar, J Paden, I Oluwanisola, L Koenig, ... 2019 IEEE International Conference on Big Data (Big Data), 5462-5468, 2019 | 19 | 2019 |
| VIRTUALOT - A framework enabling real-time coordinate transformation & occlusion sensitive tracking using UAS products, deep learning object detection & traditional object … BJ Koskowich, M Rahnemoonfar, M Starek IEEE IGARSS 2018, 2018 | 19 | 2018 |
| Deep ice layer tracking and thickness estimation using fully convolutional networks D Varshney, M Rahnemoonfar, M Yari, J Paden 2020 IEEE International conference on big data (big data), 3943-3952, 2020 | 18 | 2020 |
| Vqa-aid: Visual question answering for post-disaster damage assessment and analysis A Sarkar, M Rahnemoonfar 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 8660 …, 2021 | 17 | 2021 |
| Attention based semantic segmentation on uav dataset for natural disaster damage assessment T Chowdhury, M Rahnemoonfar 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 2325 …, 2021 | 17 | 2021 |
Masoud YariLehigh Universitylehigh.edu의 이메일 확인됨
