The Artificial Bee Colony algorithm in training Artificial Neural Network for oil spill detection

Title:

The Artificial Bee Colony algorithm in training Artificial Neural Network for oil spill detection

Creator:

Ozkan, Coskun, Ozturk, Celal, Sunar, Filiz, and Karaboga , Dervis

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:67e289cc-52f4-4388-a362-c52a96d08fb5
uuid:67e289cc-52f4-4388-a362-c52a96d08fb5
doi:10.14311/NNW.2011.21.028

Subject:

Oil spill, Artificial Neural Network (ANN), and Artificial Bee Colony (ABC)

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

Nowadays, remote sensing technology is being used as an essential tool for monitoring and detecting oil spills to take precautions and to prevent the damages to the marine environment. As an important branch of remote sensing, satellite based synthetic aperture radar imagery (SAR) is the most effective way to accomplish these tasks. Since a marine surface with oil spill seems as a dark object because of much lower backscattered energy, the main problem is to recognize and differentiate the dark objects of oil spills from others to be formed by oceanographic and atmospheric conditions. In this study, Radarsat-1 images covering Lebanese coasts were employed for oil spill detection. For this purpose, a powerful classifier, Artificial Neural Network Multilayer Perceptron (ANN MLP) was used. As the original contribution of the paper, the network was trained by a novel heuristic optimization algorithm known as Artificial Bee Colony (ABC) method besides the conventional Backpropagation (BP) and Levenberg-Marquardt (LM) learning algorithms. A comparison and evaluation of different network training algorithms regarding reliability of detection and robustness show that for this problem best result is achieved with the Artificial Bee Colony algorithm (ABC).

Language:

English

Rights:

http://creativecommons.org/publicdomain/mark/1.0/
policy:public

Source:

Neural network world: international journal on neural and mass-parallel computing and information systems | 2011 Volume:21 | Number:6

Harvested from:

CDK

Metadata only:

false