volume | PIER Journals

Abstract

For slower computation speed and lower classification accuracy of the traditional image classification methods, wavelet transform, multi-strategy, particle swarm optimization (PSO) algorithm and support vector machine (SVM) are introduced into image classification in order to propose a new remote sensing image classification (RIWMPS) method. First of all, wavelet transform method with multi-resolution characteristics is used to extract the features of remote sensing image. Then the steepest descent strategy, corrective decline strategy, random movement, aggregation strategy and diffusion strategy are used to improve the PSO algorithm to obtain an improved PSO (MSPSO) algorithm, which is used to optimize the parameters of the SVM model in order to construct an optimized SVM classifier for realizing remote sensing classification. Finally, the remote sensing image of Chongming Island is select to test the effectiveness of the RIWMPS method. The experiment results show that the RIWMPS method has higher classification efficiency and accuracy, and takes on better superiority and effectiveness. This study provides a new classification method for processing the remote sensing image.

1. Calvo, S., G. Ciraolo, and G. La Loggia, "Monitoring Posidonia oceanica meadows in a Mediterranean coastal lagoon (Stagnone, Italy) by means of nueral network and ISODATA classification methods," International Journal of Remote Sensing, Vol. 24, No. 13, 2703-2716, 2003.
doi:10.1080/0143116031000066882

2. Xia, J. S., J. Chanussot, P. J. Du, and X. Y. He, "(Semi-) Supervised probabilistic principal component analysis for hyperspectral remote sensing image classification," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 7, No. 6, 2225-2237, 2014.
doi:10.1109/JSTARS.2013.2279693

3. Dalla, M. M., A. Villa, J. A. Benediktsson, J. Chanussot, and L. Bruzzone, "Classification of hyperspectral images by using extended morphological attribute profiles and independent component analysis," IEEE Geoscience and Remote Sensing Letters, Vol. 8, No. 3, 542-546, 2011.
doi:10.1109/LGRS.2010.2091253

4. Chen, C. W., J. Luo, and K. J. Parker, "Image segmentation via adaptive K-mean clustering and knowledge-based morphological operations with biomedical applications," IEEE Transactions on Image Processing, Vol. 7, No. 12, 1673-1683, 1998.
doi:10.1109/83.730379

5. Celik, T., "Change detection in satellite images using a genetic algorithm approach," IEEE Geoscience and Remote Sensing Letters, Vol. 7, No. 2, 386-390, 2010.
doi:10.1109/LGRS.2009.2037024

6. Pandit, M., L. Srivastava, and M. Sharma, "Environmental economic dispatch in multi-area power system employing improved differential evolution with fuzzy selection," Applied Soft Computing Journal, Vol. 28, 498-510, 2015.
doi:10.1016/j.asoc.2014.12.027

7. Murthy, C. S., P. V. Raju, and K. V. S. Badrinath, "Classification of wheat crop with multi-temporal images: Performance of maximum likelihood and artificial neural networks," International Journal of Remote Sensing, Vol. 24, No. 23, 4871-4890, 2003.
doi:10.1080/0143116031000070490

8. Zhang, Y. D., Z. Dong, X. Chen, W. Jia, S. Du, K. Muhamma, and S. H. Wang, "Image based fruit category classification by 13-layer deep convolutional neural network and data augmentation," Multimedia Tools and Applications, Vol. 3, 1-20, 2017.

9. Liao, S. H., J. G. Hsieh, J. Y. Chang, and C. T. Lin, "Training neural networks via simplified hybrid algorithm mixing NelderMead and particle swarm optimization methods," Soft Computing, Vol. 19, No. 3, 679-689, 2014.
doi:10.1007/s00500-014-1292-y

10. Zhang, Y. D., L. Wu, and G. Wei, "A new classifier for polarimetric SAR images," Progress In Electromagnetics Research, Vol. 94, 83-104, 2009.
doi:10.2528/PIER09041905

11. Wang, S. H., J. Sun, P. Phillips, G. Zhao, and Y. D. Zhang, "Polarimetric synthetic aperture radar image segmentation by convolutional neural network using graphical processing units," Journal of Real-Time Image Processing, Vol. 4, 1-12, 2017.

12. Solares, C. and A. M. Sanz, "Bayesian network classifiers. An application to remote sensing image classification," WSEAS Transactions on Systems, Vol. 4, No. 4, 343-348, 2005.

13. Deng, W., H. M. Zhao, L. Zou, G. Y. Li, X. H. Yang, and D. Q. Wu, "A novel collaborative optimization algorithm in solving complex optimization problems," Soft Computing, Vol. 21, No. 15, 4387-4398, 2017.
doi:10.1007/s00500-016-2071-8

14. Lu, H., Y. Li, Y. Zhang, M. Chen, S. Serikawa, and H. Kim, "Underwater optical image processing: A comprehensive review," Mobile Networks and Applications, Vol. 22, No. 6, 1204-1211, 2017.
doi:10.1007/s11036-017-0863-4

15. Deng, W., H. M. Zhao, X. H. Yang, J. X. Xiong, M. Sun, and B. Li, "Study on an improved adaptive PSO algorithm for solving multi-objective gate assignment," Applied Soft Computing, Vol. 59, 288-302, 2017.
doi:10.1016/j.asoc.2017.06.004

16. Bazi, Y. and F. Melgani, "Toward an optimal SVM classification system for hyperspectral remote sensing images," IEEE Transactions on Geoscience and Remote Sensing, Vol. 44, No. 11, 3374-3385, 2006.
doi:10.1109/TGRS.2006.880628

17. Segata, N., E. Pasolli, F. Melgani, and E. Blanzieri, "Local SVM approaches for fast and accurate classification of remote-sensing images," International Journal of Remote Sensing, Vol. 33, No. 19, 6186-6201, 2012.
doi:10.1080/01431161.2012.678947

18. Pasolli, E., F. Melgani, D. Tuia, F. Pacifici, and W. J. Emery, "SVM active learning approach for image classification using spatial information," IEEE Transactions on Geoscience and Remote Sensing, Vol. 52, No. 4, 2217-2223, 2014.
doi:10.1109/TGRS.2013.2258676

19. Tuia, D., M. Volpi, M. D. Mura, A. Rakotomamonjy, and R. Flamary, "Automatic feature learning for spatio-spectral image classification with sparse SVM," IEEE Transactions on Geoscience and Remote Sensing, Vol. 52, No. 10, 6062-6074, 2014.
doi:10.1109/TGRS.2013.2294724

20. Adankon, M. M. and M. Cheriet, "Genetic algorithm-based training for semi-supervised SVM," Neural Computing and Applications, Vol. 19, No. 8, 1197-1206, 2010.
doi:10.1007/s00521-010-0358-8

21. Mylonas, S. K., D. G. Stavrakoudis, and J. B. Theocharis, "GeneSIS: AGA-based fuzzy segmentation algorithm for remote sensing images," Knowledge-Based Systems, Vol. 54, No. 12, 86-102, 2013.
doi:10.1016/j.knosys.2013.07.018

22. Patra, S. and L. Bruzzone, "A novel SOM-SVM-based active learning technique for remote sensing image classification," IEEE Transactions on Geoscience and Remote Sensing, Vol. 52, No. 11, 6899-6910, 2014.
doi:10.1109/TGRS.2014.2305516

23. Du, P. J., K. Tan, and X. S. Xing, "Wavelet SVM in Reproducing Kernel Hilbert Space for hyperspectral remote sensing image classification," Optics Communications, Vol. 283, No. 24, 4978-4984, 2010.
doi:10.1016/j.optcom.2010.08.009

24. Bruzzone, L., M. M. Chi, and M. Marconcini, "A novel transductive SVM for semisupervised classification of remote-sensing images," IEEE Transactions on Geoscience and Remote Sensing, Vol. 44, No. 11, 3363-3372, 2006.
doi:10.1109/TGRS.2006.877950

25. Marconcini, M., G. Camps-Valls, and L. Bruzzone, "A composite semisupervised SVM for classification of hyperspectral images," IEEE Geoscience and Remote Sensing Letters, Vol. 6, No. 2, 234-238, 2009.
doi:10.1109/LGRS.2008.2009324

26. Zhai, L., J. X. Zhang, and X. B. Yang, "Application of AdaTree algorithm to remote sensing image classification," Geomatics and Information Science of Wuhan University, Vol. 38, No. 12, 1460-1464, 2013.

27. Jing, L. H., M. F. Wang, and Q. Z. Lin, "Hyperspectral remote sensing image classification based on SVM optimized by clonal selection," Spectroscopy and Spectral Analysis, Vol. 33, No. 3, 746-751, 2013.

28. Mallinis, G. K. N., J. B. Theocharis, and V. Petridis, "SVM-based fuzzy decision trees for classification of high spatial resolution remote sensing images," IEEE Transactions on Geoscience and Remote Sensing, Vol. 50, No. 1, 149-169, 2012.
doi:10.1109/TGRS.2011.2159726

29. Xu, L. Z., C. M. Li, X.Wang, and M. G. Xu, "Object-oriented classification of hyperspectral remote sensing images based on genetic algorithm and support vector machine," Sensors and Transducers, Vol. 157, No. 10, 6-13, 2013.

30. Angrisani, I., P. Daponte, M. D. A. Puzzo, et al. "A measurement method based on the wavelet transform for power quality analysis," IEEE Transactions on Power Delivery, Vol. 13, No. 4, 990-998, 1998.
doi:10.1109/61.714415

31. Kennedy, J. and R. Eberhart, "Particle swarm optimization," Proceedings of the IEEE International Conference on Neural Networks, 1942-1948, IEEE Press, Piscataway, 1995.
doi:10.1109/ICNN.1995.488968

32. Vapnik, V., The Nature of Statistical Learning Theory, Springer Verlag, New York, 1995.
doi:10.1007/978-1-4757-2440-0

33. Liu, X. D., D. G. Jia, and H. Li, "Research on Kenel parameter optimization of support vector machine in speaker recognition," machine in speaker recognition, Vol. 10, No. 7, 1669-1673, 2010.

34. Zhang, Y. D., S. H. Wang, and G. L. Ji, "A comprehensive survey on particle swarm optimization algorithm and its applications," Mathematical Problems in Engineering, Article ID 931256, 2015.

35. Zhang, Y. D., Z. J. Yang, H. M. Lu, X. X. Zhou, P. Phillips, Q. M. Liu, and S. H. Wang, "Facial emotion recognition based on biorthogonal wavelet entropy, fuzzy support vector machine, and stratified cross validation," IEEE Access, Vol. 4, 7752782, 2016.

36. Zhang, Y. D., Y. Zhang, Y. D. Lv, X. X. Hou, F. Y. Liu, W. J. Jia, M. M. Yang, P. Phillips, and S. H. Wang, "Alcoholism detection by medical robots based on Hu moment invariants and predator-prey adaptive-inertia chaotic particle swarm optimization," Computers and Electrical Engineering, Vol. 68, 366-380, 2018.

Dr. Wu Deng

Dr. Danqin Wang

Dr. Huimin Zhao