The Open Automation and Control Systems Journal

2015, 7 : 1958-1962
Published online 2015 October 22. DOI: 10.2174/1874444301507011958
Publisher ID: TOAUTOCJ-7-1958

Oil-Spills Detection in Net-Sar Radar Images Using Support Vector Machine

Dong Zhi-Ming , Guo Li-Xia , Zeng Jian-Kui and Zhou Xue-Bin
School of Electrical & Information Engineering, Chongqing University of Science and Technology, Chongqing , 401331, P.R China.

ABSTRACT

Oil-spills detection is an important problem in many applications such as communication and navigation. Many methods have been presented for this problem. The Maximum Likelihood (ML) is one of the good solutions. But, in traditional algorithms for ML nonetheless, the computational load is very heavy and multivariate nonlinear maximization problem is serious. To deal with these problems, this paper describes an application of neural network (NN) for obtaining the global optimal solution of ML DOA estimation. It overcomes the local optima problem existing in some ML DOA estimation algorithms and improves the estimation accuracy. The computation complexity is modest.

Keywords:

Artificial Neural Network (ANN), DOA estimation, Maximum likelihood.