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30 December 1994 Estimation of sea surface velocities from space using neural networks
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Abstract
An automatic technique for estimating sea surface velocities is presented. The technique is based on pattern matching of features from successive satellite images of a common region. The patterns are matched in parallel using a Hopfield neural network. A cost function is defined in order to represent the constraints of the matching problem, and mapped onto a Hopfield net for minimisation. This method makes it possible to track deformable objects, and recursively match parts of these objects. Therefore, it gives a very precise information on the object's movement and deformation. The method has been tested on Meteosat visible images. Displacement vectors are obtained by tracking clouds on successive images. The method is shown to be faster than cross-correlation based methods, and to give denser and more precise displacement vectors along cloud edges. An extension of the method to include estimation of sea-surface velocities from sea surface temperature images is described. Future developments for automatic cloud tracking and sea surface velocities estimation are outlined in the conclusion.
© (1994) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Stephane Cote and A. R. L. Tatnall "Estimation of sea surface velocities from space using neural networks", Proc. SPIE 2315, Image and Signal Processing for Remote Sensing, (30 December 1994); https://doi.org/10.1117/12.196744
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