Ship wake-detection procedure using conjugate gradient trained artificial neural networks

Journal Article · Sun Sep 01 00:00:00 EDT 1991 · IEEE Transactions on Geoscience and Remote Sensing (Institute of Electrical and Electronics Engineers); (United States)

DOI:https://doi.org/10.1109/36.83986· OSTI ID:5293241

Fitch, J P; Lehman, S K; Dowla, F U; Lu, S Y; Johansson, E M; Goodman, D M ^[1]

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1. Lawrence Livermore National Lab., CA (United States)

This paper reports that 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.

• DOE Contract Number: W-7405-ENG-48
• OSTI ID: 5293241
• Journal Information: IEEE Transactions on Geoscience and Remote Sensing (Institute of Electrical and Electronics Engineers); (United States), Vol. 29:5; ISSN 0196-2892
• Country of Publication: United States
• Language: English

Related Subjects

02 PETROLEUM
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RESOURCES
REMOTE SENSING
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