NASA NeMO-Net

We present preliminary results from NASA NeMO-Net, the first neural multi-modal observation and training network for global coral reef assessment. NeMO-Net is an open-source deep convolutional neural network (CNN) and interactive active learning training software in development which will assess the...

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Main Author: Chirayath, Ved
Format: Other/Unknown Material
Language:unknown
Published: 2018
Subjects:
Online Access:http://hdl.handle.net/2060/20180004238
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spelling ftnasantrs:oai:casi.ntrs.nasa.gov:20180004238 2023-05-15T17:51:55+02:00 NASA NeMO-Net Chirayath, Ved Unclassified, Unlimited, Publicly available June 21, 2018 application/pdf http://hdl.handle.net/2060/20180004238 unknown Document ID: 20180004238 http://hdl.handle.net/2060/20180004238 No Copyright, Work of the U.S. Government - Public use permitted CASI Earth Resources and Remote Sensing ARC-E-DAA-TN56511 Schmidt Ocean Institute (SOI) Planning Workshop; 21 Jun. 2018; San Diego, CA; United States 2018 ftnasantrs 2019-07-20T23:14:48Z We present preliminary results from NASA NeMO-Net, the first neural multi-modal observation and training network for global coral reef assessment. NeMO-Net is an open-source deep convolutional neural network (CNN) and interactive active learning training software in development which will assess the present and past dynamics of coral reef ecosystems. NeMO-Net exploits active learning and data fusion of mm-scale remotely sensed 3D images of coral reefs captured using fluid lensing with the NASA FluidCam instrument, presently the highest-resolution remote sensing benthic imaging technology capable of removing ocean wave distortion, as well as hyperspectral airborne remote sensing data from the ongoing NASA CORAL mission and lower-resolution satellite data to determine coral reef ecosystem makeup globally at unprecedented spatial and temporal scales. Aquatic ecosystems, particularly coral reefs, remain quantitatively misrepresented by low- resolution remote sensing as a result of refractive distortion from ocean waves, optical attenuation, and remoteness. Machine learning classification of coral reefs using FluidCam mm-scale 3D data show that present satellite and airborne remote sensing techniques poorly characterize coral reef percent living cover, morphology type, and species breakdown at the mm, cm, and meter scales. Indeed, current global assessments of coral reef cover and morphology classification based on km-scale satellite data alone can suffer from segmentation errors greater than 40%, capable of change detection only on yearly temporal scales and decameter spatial scales, significantly hindering our understanding of patterns and processes in marine biodiversity at a time when these ecosystems are experiencing unprecedented anthropogenic pressures, ocean acidification, and sea surface temperature rise. NeMO-Net leverages our augmented machine learning algorithm that demonstrates data fusion of regional FluidCam (mm, cm-scale) airborne remote sensing with global low-resolution (m, km-scale) airborne and spaceborne imagery to reduce classification errors up to 80% over regional scales. Such technologies can substantially enhance our ability to assess coral reef ecosystems dynamics. Other/Unknown Material Ocean acidification NASA Technical Reports Server (NTRS)
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
Chirayath, Ved
NASA NeMO-Net
topic_facet Earth Resources and Remote Sensing
description We present preliminary results from NASA NeMO-Net, the first neural multi-modal observation and training network for global coral reef assessment. NeMO-Net is an open-source deep convolutional neural network (CNN) and interactive active learning training software in development which will assess the present and past dynamics of coral reef ecosystems. NeMO-Net exploits active learning and data fusion of mm-scale remotely sensed 3D images of coral reefs captured using fluid lensing with the NASA FluidCam instrument, presently the highest-resolution remote sensing benthic imaging technology capable of removing ocean wave distortion, as well as hyperspectral airborne remote sensing data from the ongoing NASA CORAL mission and lower-resolution satellite data to determine coral reef ecosystem makeup globally at unprecedented spatial and temporal scales. Aquatic ecosystems, particularly coral reefs, remain quantitatively misrepresented by low- resolution remote sensing as a result of refractive distortion from ocean waves, optical attenuation, and remoteness. Machine learning classification of coral reefs using FluidCam mm-scale 3D data show that present satellite and airborne remote sensing techniques poorly characterize coral reef percent living cover, morphology type, and species breakdown at the mm, cm, and meter scales. Indeed, current global assessments of coral reef cover and morphology classification based on km-scale satellite data alone can suffer from segmentation errors greater than 40%, capable of change detection only on yearly temporal scales and decameter spatial scales, significantly hindering our understanding of patterns and processes in marine biodiversity at a time when these ecosystems are experiencing unprecedented anthropogenic pressures, ocean acidification, and sea surface temperature rise. NeMO-Net leverages our augmented machine learning algorithm that demonstrates data fusion of regional FluidCam (mm, cm-scale) airborne remote sensing with global low-resolution (m, km-scale) airborne and spaceborne imagery to reduce classification errors up to 80% over regional scales. Such technologies can substantially enhance our ability to assess coral reef ecosystems dynamics.
format Other/Unknown Material
author Chirayath, Ved
author_facet Chirayath, Ved
author_sort Chirayath, Ved
title NASA NeMO-Net
title_short NASA NeMO-Net
title_full NASA NeMO-Net
title_fullStr NASA NeMO-Net
title_full_unstemmed NASA NeMO-Net
title_sort nasa nemo-net
publishDate 2018
url http://hdl.handle.net/2060/20180004238
op_coverage Unclassified, Unlimited, Publicly available
genre Ocean acidification
genre_facet Ocean acidification
op_source CASI
op_relation Document ID: 20180004238
http://hdl.handle.net/2060/20180004238
op_rights No Copyright, Work of the U.S. Government - Public use permitted
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