Fully Automatic Dark-Spot Detection From SAR Imagery With the Combination of Nonadaptive Weibull Multiplicative Model and Pulse-Coupled Neural Networks

Dark-spot detection is a critical step in oil-spill detection. In this paper, a novel approach for automated dark-spot detection using synthetic aperture radar imagery is presented. A new approach from the combination of Weibull multiplicative model (WMM) and pulse-coupled neural network (PCNN) techniques is proposed to differentiate between the dark spots and the background. First, the filter created based on WMM is applied to each subimage. Second, the subimage is segmented by PCNN techniques. As the last step, a very simple filtering process is used to eliminate the false targets. The proposed approach was tested on 60 Envisat and ERS2 images which contained dark spots. The same parameters were used in all tests. For the overall data set, an average accuracy of 93.66% was obtained. The average computational time for dark-spot detection with a 512 x 512 image is about 7 s using IDL software, which is the fastest one in this field at present. Our experimental results demonstrate that the proposed approach is very fast, robust, and effective. The proposed approach can be applied on any kind of synthetic aperture radar imagery.