Please use this identifier to cite or link to this item:
Title: Cloud detection using probabilistic neural networks
Authors: Zhang, WD
He, MX
Mak, MW 
Keywords: Atmospheric techniques
Geophysical signal processing
Image recognition
Radial basis function networks
Issue Date: 2001
Publisher: IEEE
Source: IEEE 2001 International Geoscience and Remote Sensing Symposium, 2001 : IGARSS '01, July 2001, Sydney, NSW, v. 5, p. 2373-2375 How to cite?
Abstract: This paper investigates the application of a particular type of probabilistic neural networks, namely radial basis function (RBF) networks, to detecting cloud in NOAA/AVHRR images. Based on the images collected from the East China Sea, the paper compares the performance of RBF networks with that of traditional multi-layer perceptrons (MLPs). The main results show that RBF networks are able to handle complex atmospheric and oceanographic phenomena while MLPs could not. The internal representation of the RBF networks and MLPs are also detailed in this paper
ISBN: 0-7803-7031-7
DOI: 10.1109/IGARSS.2001.978006
Appears in Collections:Conference Paper

View full-text via PolyU eLinks SFX Query
Show full item record

Page view(s)

Last Week
Last month
Citations as of Jul 10, 2018

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.