NASA NeMO-Net's Convolutional Neural Network: Mapping Marine Habitats with Spectrally Heterogeneous Remote Sensing Imagery

NASA NeMO-Net's Convolutional Neural Network: Mapping Marine Habitats with Spectrally Heterogeneous Remote Sensing Imagery

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 novel neural multimodal observation and training net...

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Bibliographic Details
Main Authors: Alan S. Li, Ved Chirayath, Michal Segal-Rozenhaimer, Juan L. Torres-Perez, Jarrett van den Bergh
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Subjects:
Convolutional neural network (CNN)
deep learning
image segmentation
multispectral imaging
Online Access:https://ieeexplore.ieee.org/document/9174766/

Internet

https://ieeexplore.ieee.org/document/9174766/

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