Browsing by Subject "Neural networks"
Now showing items 1-7 of 7
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A neural-network-fusion architecture for automatic extraction of oceanographic features from satellite remote sensing imagery
(NATO. SACLANTCEN, 1999/06)This report describes an approach for automatic feature detection from fusion of remote sensing imagery using a combination of neural network architecture and the Dempster-Shafer (DS) theory of -
Application of real data to sonar detection by neural networks
(NATO. SACLANTCEN, 1992/11)This memorandum describes the results of applying an artificial neural network for the detection of target echoes. The present study is a continuation of work conducted in 1989 in which a three-layer artificial neural ... -
Automatic target classification using multiple sidescan sonar images of different orientations
(NATO. SACLANTCEN, 1997/09)In this report, the target classification performance of a multiple view sidescan sonar is investigated. The classification statistics are estimated using model based automatic classifiers. The guidelines to the design of ... -
Exploiting phase information in synthetic aperture sonar images for target classification
(CMRE, 2019/05)It is demonstrated that the phase information present in complex high-frequency synthetic aperture sonar (SAS) imagery can be exploited for successful object classification. That is, without using the amplitude content of ... -
Mapping ocean sediments by RBF networks
(NATO. SACLANTCEN, 1992/09)Interpolation of sparse measurements of ocean-sediment properties by generalized radial basis function (RBF) networks is proposed. An RBF -
Target detection using a three-layered neural network trained by supervised back-propagation
(NATO. SACLANTCEN, 1990/05)In any sonar system a detection process has to be performed at -
Underwater target classification in synthetic aperture sonar imagery using deep convolutional neural networks
(CMRE, 2019/06)Deep convolutional neural networks are used to perform underwater target classification in synthetic aperture sonar (SAS) imagery. The deep networks are learned using a massive database of real, measured sonar data collected ...