NeMO-Net - The Neural Multi-Modal Observation & Training Network for Global Coral Reef Assessment
In the past decade, coral reefs worldwide have experienced unprecedented stresses due to climate change, ocean acidification, and anthropomorphic pressures, instigating massive bleaching and die-off of these fragile and diverse ecosystems. Furthermore, remote sensing of these shallow marine habitats...
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ftnasantrs:oai:casi.ntrs.nasa.gov:20190002246 2023-05-15T17:52:06+02:00 NeMO-Net - The Neural Multi-Modal Observation & Training Network for Global Coral Reef Assessment Segal-Rozenhaimer, Michal Das, Kamalika Li, Alan Sheng Xi Chirayath, Ved Van Den Bergh, Jarrett Torres, Juan Unclassified, Unlimited, Publicly available December 12, 2018 application/pdf http://hdl.handle.net/2060/20190002246 unknown Document ID: 20190002246 http://hdl.handle.net/2060/20190002246 Copyright, Public use permitted CASI Earth Resources and Remote Sensing ARC-E-DAA-TN64870 American Geophysical Union (AGU) Fall Meeting 2018; 10-14 Dec. 2018; Washington, DC; United States 2018 ftnasantrs 2019-07-20T23:03:25Z In the past decade, coral reefs worldwide have experienced unprecedented stresses due to climate change, ocean acidification, and anthropomorphic pressures, instigating massive bleaching and die-off of these fragile and diverse ecosystems. Furthermore, remote sensing of these shallow marine habitats is hindered by ocean wave distortion, refraction and optical attenuation, leading invariably to data products that are often of low resolution and signal-to-noise (SNR) ratio. However, recent advances in UAV and Fluid Lensing technology have allowed us to capture multispectral 3D imagery of these systems at sub-cm scales from above the water surface, giving us an unprecedented view of their growth and decay. By combining spatial and spectral information from varying resolutions, we seek to augment and improve the classification accuracy of previously low-resolution datasets at large temporal scales.NeMO-Net, the first open-source deep convolutional neural network (CNN) and interactive learning and training software, currently being developed at NASA Ames, is aimed at assessing the present and past dynamics of coral reef ecosystems through determination of percent living cover and morphology. The latest iteration uses fully convolutional networks to segment and identify coral imagery taken by UAVs and satellites, including WorldView-2 and Sentinel. We present results taken from the Indian Ocean where classification accuracy has exceeded 91% for 24 geomorphological classes given ample training data. In addition, we utilize deep Laplacian Pyramid Super-Resolution Networks (LapSRN) to reconstruct high resolution information from low resolution imagery, trained from various UAV and satellite datasets. Finally, in the case of insufficient training data, we have developed an interactive online platform that allows users to easily segment and submit their classifications, which has been integrated with the current NeMO-Net workflow. Specifically, we present results from the Fiji islands in which preliminary user data has allowed for the accurate identification of 9 separate classes, despite issues such as cloud shadowing and spectral variation. The project is being supported by NASA's Earth Science Technology Office (ESTO) Advanced Information Systems Technology (AIST-16) Program. Other/Unknown Material Ocean acidification NASA Technical Reports Server (NTRS) Indian Pyramid ENVELOPE(157.300,157.300,-81.333,-81.333) |
institution |
Open Polar |
collection |
NASA Technical Reports Server (NTRS) |
op_collection_id |
ftnasantrs |
language |
unknown |
topic |
Earth Resources and Remote Sensing |
spellingShingle |
Earth Resources and Remote Sensing Segal-Rozenhaimer, Michal Das, Kamalika Li, Alan Sheng Xi Chirayath, Ved Van Den Bergh, Jarrett Torres, Juan NeMO-Net - The Neural Multi-Modal Observation & Training Network for Global Coral Reef Assessment |
topic_facet |
Earth Resources and Remote Sensing |
description |
In the past decade, coral reefs worldwide have experienced unprecedented stresses due to climate change, ocean acidification, and anthropomorphic pressures, instigating massive bleaching and die-off of these fragile and diverse ecosystems. Furthermore, remote sensing of these shallow marine habitats is hindered by ocean wave distortion, refraction and optical attenuation, leading invariably to data products that are often of low resolution and signal-to-noise (SNR) ratio. However, recent advances in UAV and Fluid Lensing technology have allowed us to capture multispectral 3D imagery of these systems at sub-cm scales from above the water surface, giving us an unprecedented view of their growth and decay. By combining spatial and spectral information from varying resolutions, we seek to augment and improve the classification accuracy of previously low-resolution datasets at large temporal scales.NeMO-Net, the first open-source deep convolutional neural network (CNN) and interactive learning and training software, currently being developed at NASA Ames, is aimed at assessing the present and past dynamics of coral reef ecosystems through determination of percent living cover and morphology. The latest iteration uses fully convolutional networks to segment and identify coral imagery taken by UAVs and satellites, including WorldView-2 and Sentinel. We present results taken from the Indian Ocean where classification accuracy has exceeded 91% for 24 geomorphological classes given ample training data. In addition, we utilize deep Laplacian Pyramid Super-Resolution Networks (LapSRN) to reconstruct high resolution information from low resolution imagery, trained from various UAV and satellite datasets. Finally, in the case of insufficient training data, we have developed an interactive online platform that allows users to easily segment and submit their classifications, which has been integrated with the current NeMO-Net workflow. Specifically, we present results from the Fiji islands in which preliminary user data has allowed for the accurate identification of 9 separate classes, despite issues such as cloud shadowing and spectral variation. The project is being supported by NASA's Earth Science Technology Office (ESTO) Advanced Information Systems Technology (AIST-16) Program. |
format |
Other/Unknown Material |
author |
Segal-Rozenhaimer, Michal Das, Kamalika Li, Alan Sheng Xi Chirayath, Ved Van Den Bergh, Jarrett Torres, Juan |
author_facet |
Segal-Rozenhaimer, Michal Das, Kamalika Li, Alan Sheng Xi Chirayath, Ved Van Den Bergh, Jarrett Torres, Juan |
author_sort |
Segal-Rozenhaimer, Michal |
title |
NeMO-Net - The Neural Multi-Modal Observation & Training Network for Global Coral Reef Assessment |
title_short |
NeMO-Net - The Neural Multi-Modal Observation & Training Network for Global Coral Reef Assessment |
title_full |
NeMO-Net - The Neural Multi-Modal Observation & Training Network for Global Coral Reef Assessment |
title_fullStr |
NeMO-Net - The Neural Multi-Modal Observation & Training Network for Global Coral Reef Assessment |
title_full_unstemmed |
NeMO-Net - The Neural Multi-Modal Observation & Training Network for Global Coral Reef Assessment |
title_sort |
nemo-net - the neural multi-modal observation & training network for global coral reef assessment |
publishDate |
2018 |
url |
http://hdl.handle.net/2060/20190002246 |
op_coverage |
Unclassified, Unlimited, Publicly available |
long_lat |
ENVELOPE(157.300,157.300,-81.333,-81.333) |
geographic |
Indian Pyramid |
geographic_facet |
Indian Pyramid |
genre |
Ocean acidification |
genre_facet |
Ocean acidification |
op_source |
CASI |
op_relation |
Document ID: 20190002246 http://hdl.handle.net/2060/20190002246 |
op_rights |
Copyright, Public use permitted |
_version_ |
1766159431615119360 |