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An ocean color inversion model is presented for a wide variety of oceanic and coastal waters. The model is based on neural networks trained with realistic ...
PDF | On Jan 1, 2004, Wayne H. Slade Jr and others published Neural network-based retrieval of phytoplankton abundance from remotely-sensed ocean radiance.
Bibliographic details on Neural network-based retrieval of phytoplankton abundance from remotely-sensed ocean radiance.
Neural Network-based Retrieval of Phytoplankton Abundance from Remotely-sensed Ocean Radiance W.H. Slade, Jr., R.L. Miller, H. Ressom, and P. Natarajan (USA)
We report a fast model based on machine learning techniques, called Neural Network Reflectance Prediction Model (NNRPM), which can be used to predict ocean ...
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A neural network is used to model the transfer function between the concentrations of chlorophyll and sediment in Delaware Bay and the radiances received at the ...
Neural network-based retrieval of phytoplankton abundance from remotely-sensed ocean radiance. Neural Networks and Computational Intelligence 2004: 226-231.
Artificial neural networks (NNs) provide an alternative approach for retrieval of Chl from space and results for northwest European shelf seas over the 2002– ...
Neural network retrieval of phytoplankton abundance from remotely-sensed ocean radiance. In Proceedings of 2nd IASTED International Conference on Neural ...
Mar 15, 2023 · We propose to use a multi-mode Convolutional Neural Network (CNN), which can spatially learn and combine different modes, to globally account for interregional ...