Dark formation detection using recurrent neural networks and SAR data

K. Topouzelis; V. Karathanassi; P. Pavlakis; D. Rokos

doi:10.1117/12.687852

29 September 2006 Dark formation detection using recurrent neural networks and SAR data

K. Topouzelis, V. Karathanassi, P. Pavlakis, D. Rokos

Proceedings Volume 6365, Image and Signal Processing for Remote Sensing XII; 636511 (2006) https://doi.org/10.1117/12.687852
Event: SPIE Remote Sensing, 2006, Stockholm, Sweden

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.

Citation Download Citation

K. Topouzelis, V. Karathanassi, P. Pavlakis, and D. Rokos "Dark formation detection using recurrent neural networks and SAR data", Proc. SPIE 6365, Image and Signal Processing for Remote Sensing XII, 636511 (29 September 2006); https://doi.org/10.1117/12.687852

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available