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Sensors 2014, 14(12), 22798-22810; doi:10.3390/s141222798

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

Remote Sensing & Environmental Modelling Lab, Kiel University, Kiel 24098, Germany
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Received: 15 October 2014 / Revised: 18 November 2014 / Accepted: 25 November 2014 / Published: 2 December 2014
(This article belongs to the Special Issue Modern Technologies for Sensing Pollution in Air, Water, and Soil)
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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. View Full-Text
Keywords: segmentation; neural networks; dark spot detection; Synthetic Aperture Radar (SAR) segmentation; neural networks; dark spot detection; Synthetic Aperture Radar (SAR)
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Taravat, A.; Oppelt, N. Adaptive Weibull Multiplicative Model and Multilayer Perceptron Neural Networks for Dark-Spot Detection from SAR Imagery. Sensors 2014, 14, 22798-22810.

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