Weibull Multiplicative Model and Machine Learning Models for Full-automatic Dark-spot Detection from Sar Images

As a major aspect of marine pollution, oil release into the sea has serious biological and environmental impacts. Among remote sensing systems (which is a tool that offers a non-destructive investigation method), synthetic aperture radar (SAR) can provide valuable synoptic information about the position and size of the oil spill due to its wide area coverage and day/night, and allweather capabilities. In this paper we present a new automated method for oil-spill monitoring. A new approach is based on the combination of Weibull Multiplicative Model and machine learning techniques to differentiate between dark spots and the background. First, the filter created based on Weibull Multiplicative Model is applied to each sub-image. Second, the sub-image is segmented by two different neural networks techniques (Pulsed Coupled Neural Networks and Multilayer Perceptron Neural Networks). As the last step, a very simple filtering process is used to eliminate the false targets. The proposed approaches were tested on 20 ENVISAT and ERS2 images which contained dark spots. The same parameters were used in all tests. For the overall dataset, the average accuracies of 94.05 % and 95.20 % were obtained for PCNN and MLP methods, respectively. The average computational time for dark-spot detection with a 256×256 image in about 4 s for PCNN segmentation 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 to the future spaceborne SAR images International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XL-1/W3, 2013 SMPR 2013, 5 – 8 October 2013, Tehran, Iran This contribution has been peer-reviewed. The peer-review was conducted on the basis of the abstract. 421 contains dark spots. Second, the sub-images are segmented by neural networks models (PCNN or MLP) (Brekke and Solberg, 2005; Taravat et al., 2013). As the last step, a very simple filtering process is used to eliminate the false targets. 2.1 Weibull Multiplicative Model (WMM) The first step of dark feature detection is applying a filter which is used for removing image speckles and smoothing the image values. Traditionally, it has been assumed that the real and the imaginary parts of the received wave follow Gaussian distribution (Fernandes, 2001; Kuruoglu and Zerubia, 2004; Taravat et al., 2013). Another popular model is the Weibull distribution which has shown high degree of success in modeling urban scenes and sea clutter. WMM applies a nonlinear transformation to generate the texture image from the original image (Fernandes, 1998). The Weibull-distributed random variable x with form parameter > 0 and scale parameter > 0, has a probability density function given by: The m-order moment can be expressed as, For = 2, the Weibull distribution becomes a Rayleigh distribution, for = 1, it becomes an exponential distribution. It can be shown that with a > 0 is also Weibull distributed. If, with form and scale parameters given by,