Ocean Oil Spill Classification with RADARSAT-2 SAR Based on an Optimized Wavelet Neural Network

Details

• Journal Title:
  Remote Sensing
• Personal Author:
  Song, Dongmei ; Ding, Yaxiong ; Li, Xiaofeng ; Zhang, Biao ; Xu, Mingyu
• NOAA Program & Office:
  NESDIS (National Environmental Satellite, Data, and Information Service) ; STAR (Center for Satellite Applications and Research)
• Description:
  Oil spill accidents from ship or oil platform cause damage to marine and coastal environment and ecosystems. To monitor such spill events from space, fully polarimetric (Pol-SAR) synthetic aperture radar (SAR) has been greatly used in improving oil spill observation. Aiming to promote ocean oil spill classification accuracy, we developed a new oil spill identification method by combining multiple fully polarimetric SAR features data with an optimized wavelet neural network classifier (WNN). Two sets of RADARSAT-2 fully polarimetric SAR data are applied to test the validity of the developed method. The experimental results show that: (1) the convergence ability of optimized WNN can be enhanced, improving overall classification accuracy of ocean oil spill, in comparison to the classification results based on a common un-optimized WNN classifier; and (2) the joint use of the multiple fully Pol-SAR features as the inputs of the classifier can achieve better classification result than that only with single fully Pol-SAR feature. The developed method can improve classification accuracy by 4.96% and 7.75%, compared with the classification results with un-optimized WNN and only with one single fully polarimetric SAR feature. The classification overall accuracy based on the proposed approach can reach 97.67%. Experimental results have proven that the proposed approach is effective and applicable to classify the ocean oil spill.
• Keywords:
  Oil Spills Remote Sensing Oil Spills Remote Sensing
• Source:
  Remote Sens. 2017, 9(8), 799
• DOI:
  https://doi.org/10.3390/rs9080799
• Document Type:
  Journal Article
• Rights Information:
  CC BY
• Compliance:
  Submitted
• Main Document Checksum:
  urn:sha256:51ab6edb674ddab08820dee7549495394e682032100cbf84c91a0cdcc1840530
• Download URL:
  https://repository.library.noaa.gov/view/noaa/43959/noaa_43959_DS1.pdf
• File Type:
  [PDF - 5.57 MB ]