Please use this identifier to cite or link to this item:
|Title:||Dark Formation Detection using Recurrent Neural Networks and SAR Data|
|Authors:||TOPOUZELIS KONSTANTINOS; KARATHANASSI Vassilia; PAVLAKIS Petros; ROKOS Demetrius|
|Citation:||Image and Signal Processing for Remote Sensing XII vol. 6365 p. 636511-1 636511-7|
|Publisher:||SPIE Digital Libriary|
|Type:||Articles in periodicals and books|
|Abstract:||In this paper a classification scheme based on recurrent neural networks is presented. Neural networks may be viewed as a mathematical model composed of many non-linear computational elements, called neurons, operating in parallel and massively connected by links characterized by different weights. It is well known that conventional feedforward neural networks can be used to approximate any spatially finite function given a set of hidden nodes. Recurrent neural networks are fundamentally different from feedforward architectures in the sense that they not only operate on an input space but also on an internal state space – a trace of what already has been processed by the network. This capability is referred as internal memory of the recurrent networks. The general objectives of this paper are to describe, demonstrate and test the potential of simple recurrent artificial neural networks for dark formation detection using SAR satellite images over the sea surface. The type and the architecture of the network are subjects of research. Input to the networks is the original SAR image. The network is called to classify the image into dark formations and clean sea. Elman’s and Jordan’s recurrent networks have been examined. Jordan’s networks have been recognized as more suitable for dark formation detection. The Jordan’s specific architecture with five inputs, three hidden neurons and one output is proposed for dark formation detection as it classifies correctly more than 95.5% of the data set.|
|JRC Institute:||Institute for the Protection and Security of the Citizen|
Files in This Item:
There are no files associated with this item.
Items in repository are protected by copyright, with all rights reserved, unless otherwise indicated.