DocumentCode
1327725
Title
Ship wake-detection procedure using conjugate gradient trained artificial neural networks
Author
Fitch, J.P. ; Lehman, S.K. ; Dowla, F.U. ; Lu, S.Y. ; Johansson, E.M. ; Goodman, D.M.
Author_Institution
Lawrence Livermore Nat. Lab., California Univ., CA, USA
Volume
29
Issue
5
fYear
1991
fDate
9/1/1991 12:00:00 AM
Firstpage
718
Lastpage
726
Abstract
A method has been developed to reduce large two-dimensional images to significantly smaller feature lists. These feature lists overcome the problem of storing and manipulating large amounts of data. A new artificial neural network using conjugate gradient training methods, operating on sets of feature lists, was successfully trained to determine the presence or absence of wakes in synthetic aperture radar images. A comparison has been made between the different conjugate gradient and steepest-descent training methods and has demonstrated the superiority of the former over the latter
Keywords
computerised pattern recognition; computerised picture processing; conjugate gradient methods; geophysics computing; neural nets; ocean waves; oceanographic techniques; remote sensing by radar; conjugate gradient trained artificial neural networks; feature lists; large two-dimensional images; ocean waves; remote sensing; ships; synthetic aperture radar images; wake-detection procedure; Artificial neural networks; Marine vehicles; Ocean temperature; Pattern recognition; Pixel; Radar detection; Radar imaging; Sea surface; Spaceborne radar; Surface waves;
fLanguage
English
Journal_Title
Geoscience and Remote Sensing, IEEE Transactions on
Publisher
ieee
ISSN
0196-2892
Type
jour
DOI
10.1109/36.83986
Filename
83986
Link To Document