NDLI: Assessing the ocean surface vector wind signal in SSM/I data using neural networks

Content Provider	IEEE Xplore Digital Library
Author	Bates, J. Chesley McColl, K.
Copyright Year	1996
Description	Author affiliation: Climate Diagnostics Center, NOAA, Boulder, CO, USA (Bates, J.)
Abstract	Minimization techniques have long been used by the remote sensing community to obtain relationships between observed radiances and retrieved geophysical quantities. Originally, special sensor microwave imager (SSM/I) passive microwave wind observations were regressed against coincident wind speed observations from buoys. More recent research has shown that a wind direction signal may also be present in SSM/I observations. This wind direction signal, however, is not very large, meaning that accounting for the error characteristics of this signal is very important, and it is believed that the signal is somewhat nonlinear. Thus, more advanced minimization techniques are required to extract this wind direction signal. The paper discusses neural networks (NN) which are a nonlinear minimization technique and they can be used either in simulation mode, where the net is trained using simulated radiances assumed to be the truth, or in observation mode, where the observed radiances are matched with in situ observations.
Starting Page	1710
Ending Page	1711
File Size	151290
Page Count	2
File Format	PDF
ISBN	0780330684
DOI	10.1109/IGARSS.1996.517861
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1996-05-31
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Sea surface Intelligent networks Neural networks Wind speed Ocean temperature Brightness temperature Polarization Passive microwave remote sensing Image sensors Microwave sensors
Content Type	Text
Resource Type	Article

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