Abstract
This paper intends to find a more cost-effective way for training oil spill classification systems by introducing active learning (AL) and exploring its potential, so that satisfying classifiers could be learned with reduced number of labeled samples. The dataset used has 143 oil spills and 124 look-alikes from 198 RADARSAT images covering the east and west coasts of Canada from 2004 to 2013. Six uncertainty-based active sample selecting (ACS) methods are designed to choose the most informative samples. A method for reducing information redundancy amongst the selected samples and a method with varying sample preference are considered. Four classifiers (k-nearest neighbor (KNN), support vector machine (SVM), linear discriminant analysis (LDA) and decision tree (DT)) are coupled with ACS methods to explore the interaction and possible preference between classifiers and ACS methods. Three kinds of measures are adopted to highlight different aspect of classification performance of these AL-boosted classifiers. Overall, AL proves its strong potential with 4% to 78% reduction on training samples in different settings. The SVM classifier shows to be the best one for using in the AL frame, with perfect performance evolving curves in different kinds of measures. The exploration and exploitation criterion can further improve the performance of the AL-boosted SVM classifier but not of the other classifiers.
Highlights
With the increase of maritime traffic, the accidental and deliberate discharge of oil from ships is attracting growing concern
In this study based on a ten-year RADARSAT dataset covering west and east coasts of Canada, active learning (AL) has shown its great potential of training sample reduction in constructing oil spill classifiers
That means the real-world projects of constructing oil spill classification systems or improving existed systems may benefit from using AL methods
Summary
With the increase of maritime traffic, the accidental and deliberate discharge of oil from ships is attracting growing concern. Automatic oil spill classification system with real-time, fully operational and wider water coverage capability is needed [1], as Solberg et al state [4] “The currently manual services is just a first step toward a fully operational system covering wider waters”. Due to their ability to smooth sea surface, oil spills usually appear as dark spots on SAR images. In an automatic or semiautomatic SAR oil spill detection system, three steps are sequentially performed to identify oil spills [1,5,6,7,8]: (i) dark-spot detection for identifying all candidates that belong to either oil spills or look-alikes; (ii) feature extraction for collecting object-based features, such as the mean intensity value of dark-spots, for discriminating oil spills and look-alikes; and (iii) classification for separating oil spills and look-alikes using the features extracted
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