Dark formation detection using neural networks
Abstract:Synthetic Aperture Radar (SAR) images are extensively used for dark formation detection in marine environment, as they are not affected by local weather conditions and cloudiness. Dark formations can be caused by man-made actions (e.g. oil spills) or natural ocean phenomena (e.g. natural slicks and wind front areas). Radar backscatter values for oil spills are very similar to backscatter values for very calm sea areas and other ocean phenomena because they dampen the capillary and short gravity sea waves. Thus, traditionally, dark formation detection is the first stage of the oil-spill detection procedure and in most studies is performed manually or using a fixed size window in which a threshold value is adopted. In high-resolution imagery, dark formation detection may fail due to the nonlinear behaviour of the pixel values contained in the dark formation and in the area around it. In this paper, we examine the ability of two feed-forward neural network families, i.e. Multilayer Perceptron (MLP) and the Radial Basis Function (RBF) networks, to detect dark formations in high-resolution SAR images. The general objective of this paper is to test the potential of artificial neural networks for dark formation detection using SAR high-resolution satellite images. Both the type and the architecture of the network are subjects of research. The inputs into the networks are the original SAR images. Each network is called to classify an area of the image as dark area or sea. The group of MLP networks can be recognized as the most suitable group for dark formation detection, as it presents reliable stable results for all the examined accuracies. Nevertheless, in terms of single topology, there is no an MLP topology that performs significantly better than the others.
Document Type: Research Article
Affiliations: 1: European Commission, Joint Research Centre, Institute for the Protection and Security of the Citizen, Sensors, Radar Technologies and Cybersecurity Unit, Via Fermi 2749, T.P. 670 - Ispra (VA), 21027, Italy 2: Laboratory of Remote Sensing, School of Rural and Surveying Engineering, National Technical University of Athens, Zographos, 15780, Greece 3: Hellenic Centre for Marine Research, 19013 Anavissos, Greece
Publication date: January 1, 2008