Username   Password       Forgot your password?  Forgot your username? 


Imbalanced Remote Sensing Ship Image Classification

Volume 15, Number 6, June 2019, pp. 1709-1715
DOI: 10.23940/ijpe.19.06.p22.17091715

Sizhe Huanga,b, Huosheng Xub, and Xuezhi Xiab

aCollege of Information Technology, Harbin Engineering University, Harbin, 150000, China
bWuhan Digital Engineering Research Institute, Wuhan, 430000, China


(Submitted on December 15, 2018; Revised on January 16, 2019; Accepted on February 11, 2019)


Aiming at the unbalanced classification problem of remote sensing ship image datasets in ship target classification and the problem that the traditional decision tree classification algorithm needs to rely on artificial construction features to realize classification, a weighted deep neural decision forest is proposed. This method combines deep learning with resampling. The results show that the method can achieve a better classification accuracy than the traditional decision tree on unbalanced classification of ship target.


References: 11

  1. M. Galar, A. Fernández, E. Barrenechea, H. Bustince, and F. Herrera, “A Review on Ensembles for Class Imbalance Problem: Bagging, Boosting and Hybrid Based Approaches,” IEEE Transactions on Systems, Vol. 42, No. 4, pp. 463-484, 2012
  2. S. Huang, X. Xia, and H. Xu, “A Remote Sensing Ship Recognition using Random Forest,” in Proceedings of the 4th International Conference on Information Science and Cloud Computing (ISCC2015), Guangzhou, China, December 2015
  3. Y. Bengio, O. Delalleau, and C. Simard, “Decision Trees do not Generalize to New Variations,” Computational Intelligence, Vol. 26, No. 4, pp. 449-467, 2010
  4. P. Kontschieder, M. Fiterau, A. Crimisi, and S. Bulò, “Deep Neural Decision Forests,” in Proceedings of IEEE International Conference on Computer Vision, pp. 1467-1475, 2015
  5. S. Bulò and P. Kontschieder, “Neural Decision Forests for Semantic Image Labelling,” in Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition, pp. 81-88, 2014
  6. C. Corbane, L. Najman, and E. Pecoul, “A Complete Processing Chain for Ship Detection using Optical Satellite Imagery,” International Journal of Remote Sensing, Vol. 31, No. 22, pp. 5837-5854, 2010
  7. B. S. Manjunath and B. Sumengen, “Graph Partitioning Active Contours (GPAC) for Image Segmentation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 28, No. 4, pp. 509-521, 2006
  8. J. X. Sun, “Modern Pattern Recognition,” 2nd Edition, Higher Education Publishing Company, Beijing, 2008
  9. G. C. Anagnostopoulos, “SVM-based Target Recognition from Synthetic Aperture Radar Images using Target Region Outline Descriptors,” Nonlinear Anal, Vol. 71, No. 12, pp. 2934-2939, 2009
  10. B. J. Frey and D. Dueck, “Clustering by Passing Messages Between Data Points,” Science, Vol. 315, No. 5814, pp. 972-976, 2007
  11. W. Guo, X. Xia, and X. Wang, “A Remote Sensing Ship Recognition Method of Entropy-based Hierarchical Discriminant Regression,” Optik-International Journal for Light and Electron Optics, Vol. 126, No. 20, pp. 2300-2307, 2015


Please note : You will need Adobe Acrobat viewer to view the full articles.Get Free Adobe Reader

This site uses encryption for transmitting your passwords.