NDLI: Adaptive Weibull Multiplicative Model and Multilayer Perceptron Neural Networks for Dark-Spot Detection from SAR Imagery

Content Provider	PubMed Central
Author	Taravat, Alireza Oppelt, Natascha
Copyright Year	2014
Abstract	Oil spills represent a major threat to ocean ecosystems and their environmental status. Previous studies have shown that Synthetic Aperture Radar (SAR), as its recording is independent of clouds and weather, can be effectively used for the detection and classification of oil spills. Dark formation detection is the first and critical stage in oil-spill detection procedures. In this paper, a novel approach for automated dark-spot detection in SAR imagery is presented. A new approach from the combination of adaptive Weibull Multiplicative Model (WMM) and MultiLayer Perceptron (MLP) neural networks is proposed to differentiate between dark spots and the background. The results have been compared with the results of a model combining non-adaptive WMM and pulse coupled neural networks. The presented approach overcomes the non-adaptive WMM filter setting parameters by developing an adaptive WMM model which is a step ahead towards a full automatic dark spot detection. The proposed approach was tested on 60 ENVISAT and ERS2 images which contained dark spots. For the overall dataset, an average accuracy of 94.65% was obtained. Our experimental results demonstrate that the proposed approach is very robust and effective where the non-adaptive WMM & pulse coupled neural network (PCNN) model generates poor accuracies.
Related Links	http://dx.doi.org/10.3390/s141222798
Ending Page	22810
Page Count	13
Starting Page	22798
File Format	PDF
ISSN	14248220
e-ISSN	14248220
Journal	Sensors (Basel, Switzerland)
Issue Number	12
Volume Number	14
Language	English
Publisher	MDPI
Publisher Date	2014-12-01
Access Restriction	Open
Rights Holder	MDPI
Subject Keyword	Electrical and Electronic Engineering Analytical Chemistry Atomic and Molecular Physics, and Optics Biochemistry Research in Higher Education
Content Type	Text
Resource Type	Article
Subject	Medicine Atomic and Molecular Physics, and Optics Instrumentation Analytical Chemistry Biochemistry Information Systems Electrical and Electronic Engineering

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