SAR oil spills image recognition based on RBF network model

  • ZHOU Hui ,
  • CHEN Peng
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  • (1.Department of Software Engineering,Dalian Neusoft Information University, Dalian 116023, China;2.Navigation College,Dalian Maritime University, Dalian 116026,China)

Received date: 2017-07-20

  Revised date: 2017-11-13

  Online published: 2017-11-14

Abstract

Radial basis function (RBF) neural network model was used to distinguish oil slicks or look-alikes oil slicks to provide an important prerequisite for oil spill decision. Firstly, the efficient eigenvectors was extracted from synthetic aperture radar (SAR) images to acquire eigenvectors, and eigenvectors were used as input layer parameters to establish excitation function. Secondly,the RBF neural network model was trained by SAR image samples, and the error between output value and actual value was used as a constraint condition to adjust the weight factor, radial basis center and width, and estimate the oil spill situation according to the linear activation function value of output layer. Experimental results show that the accuracy rate of RBF model is more than 90% in recognition of “oil slicks” and “look-alikes oil slicks” image. The results also reveal that the outputs from the RBF neural network are more accurate compared to those from the BP neural network.

Cite this article

ZHOU Hui , CHEN Peng . SAR oil spills image recognition based on RBF network model[J]. Journal of Dalian Maritime University, 2018 , 44(2) : 113 -117 . DOI: 10.16411/j.cnki.issn1006-7736.2018.02.017

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