Combined Method of an Efficient Cuckoo Search Algorithm and Nonnegative Matrix Factorization of Different Zernike Moment Features for Discrimination Between Oil Spills and Lookalikes in SAR Images

Abstract

Oil spill detection in synthetic aperture radar (SAR) images plays a crucial role in oil slick monitoring on the sea surface. In addition to oil spills, SAR images incorporate a large number of similar phenomena called “lookalikes”. Choosing a method of discriminating between oil spills and lookalikes with high accuracy is a necessity. In this paper, a new algorithm is proposed for discrimination between oil spills and lookalikes, which consists of the following four main parts. 1) Dark spot detection in SAR images. 2) Shape-based feature extraction of oil spills and lookalikes selected from dark spots in the previous stage. 3) Feature transformation using the NMF algorithm. 4) Classification. To evaluate the performance of the proposed algorithm, it was tested on the several real SAR images. This algorithm was able to provide promising results for both dark spot detection and oil spill classification. The average error rate of dark spot detection for a typical image was 1.31% and it could discriminate between oil spills and lookalikes with an average accuracy of 96.31% using feature transformation modes after transforming the features and classifying them using the K-nearest neighbor classifier.