Zobrazeno 1 - 10
of 57
pro vyhledávání: '"Maryam Rahnemoonfar"'
Publikováno v:
Scientific Data, Vol 10, Iss 1, Pp 1-9 (2023)
Abstract Recent advancements in computer vision and deep learning techniques have facilitated notable progress in scene understanding, thereby assisting rescue teams in achieving precise damage assessment. In this paper, we present RescueNet, a metic
Externí odkaz:
https://doaj.org/article/a4c057e9b0c647b181085eb3257df665
Autor:
Farshad Safavi, Maryam Rahnemoonfar
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 16, Pp 4-20 (2023)
Real-time semantic segmentation of aerial imagery is essential for unmanned ariel vehicle applications, including military surveillance, land characterization, and disaster damage assessments. Recent real-time semantic segmentation neural networks pr
Externí odkaz:
https://doaj.org/article/6aa18bde807c4d848ad080e3e5acf920
Publikováno v:
Journal of Glaciology, Vol 67, Pp 39-48 (2021)
In this study, our goal is to track internal ice layers on the Snow Radar data collected by NASA Operation IceBridge. We examine the application of deep learning methods on radar data gathered from polar regions. Artificial intelligence techniques ha
Externí odkaz:
https://doaj.org/article/be4c6b32316a49288f9c587e06270b4d
Autor:
Maryam Rahnemoonfar, Tashnim Chowdhury, Argho Sarkar, Debvrat Varshney, Masoud Yari, Robin Roberson Murphy
Publikováno v:
IEEE Access, Vol 9, Pp 89644-89654 (2021)
Visual scene understanding is the core task in making any crucial decision in any computer vision system. Although popular computer vision datasets like Cityscapes, MS-COCO, PASCAL provide good benchmarks for several tasks (e.g. image classification,
Externí odkaz:
https://doaj.org/article/de2388e23953411c95be84c370c38d76
Autor:
Masoud Yari, Oluwanisola Ibikunle, Debvrat Varshney, Tashnim Chowdhury, Argho Sarkar, John Paden, Jilu Li, Maryam Rahnemoonfar
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 14, Pp 12035-12047 (2021)
Monitoring properties of ice sheets in polar regions is one of the main challenges in glaciology. There is a large amount of heterogeneous radar data from the polar regions that have been gathered through expensive missions. However, retrieving meani
Externí odkaz:
https://doaj.org/article/5477ceb33a1246e6b83ff2d48f82b74d
Autor:
Debvrat Varshney, Maryam Rahnemoonfar, Masoud Yari, John Paden, Oluwanisola Ibikunle, Jilu Li
Publikováno v:
Remote Sensing, Vol 13, Iss 14, p 2707 (2021)
Climate change is extensively affecting ice sheets resulting in accelerating mass loss in recent decades. Assessment of this reduction and its causes is required to project future ice mass loss. Annual snow accumulation is an important component of t
Externí odkaz:
https://doaj.org/article/2ae1e73dfda2403d86b1730d1b0e0817
Autor:
Maryam Rahnemoonfar
Publikováno v:
Electronic Journal of Differential Equations, Vol 2016, Iss 23, Pp 119-129 (2016)
Phase unwrapping is the most critical step in the processing of synthetic aperture radar interferometry. The phase obtained by SAR interferometry is wrapped over a range from $-\pi$ to $\pi$. Phase unwrapping must be performed to obtain the true p
Externí odkaz:
https://doaj.org/article/6277234aee644fbf81680ea4af6083aa
Publikováno v:
Remote Sensing, Vol 11, Iss 9, p 1128 (2019)
Recent deep-learning counting techniques revolve around two distinct features of data—sparse data, which favors detection networks, or dense data where density map networks are used. Both techniques fail to address a third scenario, where dense obj
Externí odkaz:
https://doaj.org/article/e4b97667cce9453a821fa6bf0ec89446
Autor:
Argho Sarkar, Maryam Rahnemoonfar
Publikováno v:
2022 IEEE International Conference on Image Processing (ICIP).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::97fd44ed9c380960efc5d2e01baa4c92
https://doi.org/10.1109/icip46576.2022.9897381
https://doi.org/10.1109/icip46576.2022.9897381
Publikováno v:
IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2e1453bf9c20f78cfed2e1938e62b461
https://doi.org/10.1109/igarss46834.2022.9884370
https://doi.org/10.1109/igarss46834.2022.9884370