Zobrazeno 1 - 10
of 19
pro vyhledávání: '"Ved Chirayath"'
Autor:
Lonneke Goddijn-Murphy, Victor Martínez-Vicente, Heidi M. Dierssen, Valentina Raimondi, Erio Gandini, Robert Foster, Ved Chirayath
Publikováno v:
Remote Sensing, Vol 16, Iss 10, p 1770 (2024)
Most advances in the remote sensing of floating marine plastic litter have been made using passive remote-sensing techniques in the visible (VIS) to short-wave-infrared (SWIR) parts of the electromagnetic spectrum based on the spectral absorption fea
Externí odkaz:
https://doaj.org/article/241a7d74450e4ab69b3969c76d6db535
Autor:
Alan S. Li, Ved Chirayath, Michal Segal-Rozenhaimer, Juan L. Torres-Perez, Jarrett van den Bergh
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 13, Pp 5115-5133 (2020)
Recent advances in machine learning and computer vision have enabled increased automation in benthic habitat mapping through airborne and satellite remote sensing. Here, we applied deep learning and neural network architectures in NASA NeMO-Net, a no
Externí odkaz:
https://doaj.org/article/af3aae93efe844f69eec1a05b1d2f857
Autor:
Jarrett van den Bergh, Ved Chirayath, Alan Li, Juan L. Torres-Pérez, Michal Segal-Rozenhaimer
Publikováno v:
Frontiers in Marine Science, Vol 8 (2021)
NASA NeMO-Net, The Neural Multimodal Observation and Training Network for global coral reef assessment, is a convolutional neural network (CNN) that generates benthic habitat maps of coral reefs and other shallow marine ecosystems. To segment and cla
Externí odkaz:
https://doaj.org/article/5e2ede03068841858ce44cc2a99e0967
Autor:
Ved Chirayath, Alan Li
Publikováno v:
Frontiers in Marine Science, Vol 6 (2019)
We highlight three emerging NASA optical technologies that enhance our ability to remotely sense, analyze, and explore ocean worlds–FluidCam and fluid lensing, MiDAR, and NeMO-Net. Fluid lensing is the first remote sensing technology capable of ima
Externí odkaz:
https://doaj.org/article/220e2611ba5b40b8bfd855cc88e01f4f
Autor:
Sam Purkis, Ved Chirayath
Publikováno v:
Annual Review of Environment and Resources. 47:823-847
This article reviews the broad range of contemporary remote sensing technologies that can access the ocean, while emphasizing next-generation ones that might revolutionize the field. Significant challenges remain in studying the largest part of Earth
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 13:5115-5133
Recent advances in machine learning and computer vision have enabled increased automation in benthic habitat mapping through airborne and satellite remote sensing. Here, we applied deep learning and neural network architectures in NASA NeMO-Net, a no
Publikováno v:
Frontiers in Marine Science, Vol 8 (2021)
NASA NeMO-Net, the Neural Multimodal Observation and Training Network for global coral reef assessment, is a convolutional neural network (CNN) that generates benthic habitat classification maps for coral reef and other shallow marine ecosystems from
Autor:
Alan Li, Andrew G. Schmidt, Matthew French, Ved Chirayath, Saquib A. Siddiqui, Sanil Rao, Vivek V. Menon
Publikováno v:
2021 IEEE Aerospace Conference (50100).
Multispectral, Imaging, Detection, and Active Reflectance (MiDAR) is a method capable of imaging through ocean waves without distortion in 3D at sub-cm resolutions to sense living and non-living structures in light-limited and analog planetary scienc
Autor:
Ved Chirayath
Publikováno v:
OSA Optical Sensors and Sensing Congress 2021 (AIS, FTS, HISE, SENSORS, ES).
Fluid lensing, a passive remote sensing technology, exploits refractive fluid distortions and caustics caused by ocean waves for cm-scale 3D imaging of coral reefs. Results at 45ft depth are presented from a recent Guam campaign.
Autor:
Alex Dehgan, Bunje Paul, Tali Treibitz, David J. Kriegman, J. Emmett Duffy, David I. Kline, Oren Levy, Andreas J. Andersson, Oscar Pizarro, Sean R. Connolly, Matthieu Leray, Pim Bongaerts, Shah Selbe, Melanie McField, Ved Chirayath
Publikováno v:
Marine Technology Society Journal. 55:118-119
Up to 90% of global coral reefs are predicted to be severely degraded by 2050 under “business-as-usual” scenarios. To meet the scale and scope of this challenge, we propose designing and demonstrating a multi-modal system that can incorporate dat