Next Article in Journal
On the MAC/Network/Energy Performance Evaluation of Wireless Sensor Networks: Contrasting MPH, AODV, DSR and ZTR Routing Protocols
Next Article in Special Issue
Application of Gas Sensor Arrays in Assessment of Wastewater Purification Effects
Previous Article in Journal
GaAs Coupled Micro Resonators with Enhanced Sensitive Mass Detection
Previous Article in Special Issue
Investigation into Alternative Sample Preparation Techniques for the Determination of Heavy Metals in Stationary Source Emission Samples Collected on Quartz Filters
Article Menu

Export Article

Open AccessArticle
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
Author to whom correspondence should be addressed.
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)
View Full-Text   |   Download PDF [3771 KB, uploaded 2 December 2014]   |  


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)

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).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

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.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top