Oil spill detection using synthetic aperture radar images and feature selection in shape space

Highlights

• The major goal of the study is to discriminate oil spills from look-alikes.

• We devote to find the optimal feature set in shape space.

• Our method gets the important eigenvalues (IEs), those useful to discriminate the targets.

• Compared to other methods, this method is effective, and the recognition accuracy is relatively high.

Abstract

The major goal of the present study is to describe a method by which synthetic aperture radar (SAR) images of oil spills can be discriminated from other phenomena of similar appearance. The optimal features of these dark formations are here identified. Because different materials have different physical properties, they form different shapes. In this case, oil films and lookalike materials have different fluid properties. In this paper, 9 shape features with a total of 95 eigenvalues were selected. Using differential evolution feature selection (DEFS), similar eigenvalues were extracted from total space of oil spills and lookalike phenomena. This process assumes that these similar eigenvalues impair classification. These similar eigenvalues are removed from the total space, and the important eigenvalues (IEs), those useful to the discrimination of the targets, are identified. At least 30 eigenvalues were found to be inappropriate for classification of our shape spaces. The proposed method was found to be capable of facilitating the selection of the top 50 IEs. This allows more accurate classification. Here, accuracy reached 94%. The results of the experiment show that this novel method performs well. It could also be made available to teams across the world very easily.

Introduction

An oil spill is the release of a liquid petroleum hydrocarbon into the environment, especially marine areas, due to human activity, and is a form of pollution. Oil spills are a major threat to marine ecosystems. Space-borne SAR can be used to gather information about maritime oil spills in any type of weather. Previous works have focused on the dark areas in SAR images, but oil spills are not the only things that can cause them. Darker areas may also be caused by other factors, such as currents, eddies, up- and down-welling, ice, wind fronts, sheltering provided by land, rain cells, and internal waves (Brekke and Solberg, 2005a, Brekke and Solberg, 2005b, Fiscella et al., 2000, Nirchio et al., 2005, Ferraro et al., 2007, Ferraro et al., 2009, Karathanassi et al., 2006). These objects (dark areas) resembling oil spills are called “lookalikes” (Brekke and Solberg, 2005a, Brekke and Solberg, 2005b, Topouzelis et al., 2008).

Oil films and lookalikes all have texture features, gray features, and frequency domain characteristics. Several researchers have used multi-features to detect oil spill from lookalikes. Fiscella et al. (2000) used 14 geometrical characteristics for both oil spill and natural features. Solberg and Theophilopoulos (1997) selected 11 features including surroundings and shape. Del Frate et al. (2000) considered 11 geometrical features in terms of its extension and its shape. A general description of the features calculated was published by Espedal and Johannessen (2000). They were the first to introduce texture features. Keramitsoglou et al. (2005) described 14 features. Karathanassi et al. (2006) described 13 features. Both these teams addressed physical, geometrical, and textural behavior. Several schemes have been designed for the purpose of unifying features with similar characteristics (Brekke and Solberg, 2005a, Brekke and Solberg, 2005b, Migliaccio and Trangaglia, 2004, Montali et al., 2006). Most of the published papers that describe attempts to detect oil spills discuss features such as shape, physical characteristics, and texture. However, none of the methods described in these studies addressed the issue of which features are useful and which are not.

Because oil films and lookalikes involve different materials, they have different physical properties and may take different shapes under certain wave and current conditions. Alessandro et al. (2007) simulate shift of oil-films shape. It indicates that shape features of oil films tend to be regular. In the present work, 9 space features were selected: marking ratio, solidity, rectangular saturation, circularity, narrowness, and edge density, interior angles based on bounding polygons (IABP), Hu moment invariance, and elliptical Fourier descriptors. These features were used to detect the oil spills from lookalikes. The results suggest that not all selected features have an effect on the classification process. The purpose of the present work was to identify the most important features (IEs) of the oil spills using differential evolution and a statistical repair mechanism (Rami et al., 2007, Rami et al., 2011). Then test samples were re-tested with IEs.

This paper is organized in the following manner. Section 2 introduces the method under discussion, and Section 3 shows the dataset. Results and the contributions to the detection of oil spill are discussed in Sections 4 Experimental results, 5 Evaluation of contributions, respectively. The paper is concluded in Section 6.