Abstract
With the fast development of remote sensing techniques, the spatial resolution of remote sensed image are improved significantly. However, the excessive spatial resolution leads to a sharp increase in data volume and spectral information confusion of objects. The multi-scale kernel learning (MSKL) method has shown an excellent advantage in classification of high-resolution satellite image. Nevertheless, the performance of the MSKL is dramatically influenced by the widths and weights of the Radial Basis Function (RBF) kernel, since its multi-scale kernel function is constructed by several RBF kernels. In order to achieve efficient multi-scale classifier, a new dynamic differential evolution (DE) algorithm is introduced in this paper. In addition, the spectral features and spatial fractal texture features of images are synthetically employed to construct the multi-scale kernel. The experimental results show that the multi-scale kernel based on the dynamic DE algorithm is superior to the traditional multi-scale kernel in obtaining a better multi-scale kernel classifier and with higher classification accuracy.
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Yuan, J., Wang, D., Li, R.: Remote sensing image segmentation by combining spectral and texture features. IEEE Trans. Geosci. Remote Sens. 52(1), 16–24 (2014)
Yan, Y., Ren, J., Sun, G., et al.: Unsupervised image saliency detection with Gestalt-laws guided optimization and visual attention based refinement. Pattern Recogn. 79, 65–78 (2018)
Han, J., Zhang, D., Hu, X., et al.: Background prior-based salient object detection via deep reconstruction residual. IEEE Trans. Circuits Syst. Video Technol. 25(8), 1309–1321 (2015)
Vapnik, V.: The Nature of Statistical Learning Theory. Springer, New York (1995)
Vapnik, V.: Statistical Learning Theory. Wiley, New York (1998)
LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)
Jawad, H., Olivier, P., Ren, J., et al.: Performance of hidden Markov model and dynamic Bayesian network classifiers on handwritten Arabic word recognition. Knowl. Based Syst. 24(5), 680–688 (2011)
Wang, Z., Ren, J., Zhang, D., et al.: A deep-learning based feature hybrid framework for spatiotemporal saliency detection inside videos. Neurocomputing 287, 68–83 (2018)
Ren, J.: ANN vs SVM: which one performs better in classification of MCCs in mammogram imaging. Knowl. Based Syst. 26, 144–153 (2012)
Zheng, D., Wang, J., Zhao, Y.: Non-flat function estimation with a multi-scale support vector regession. Neurocomputing 70(1–3), 420–429 (2006)
Schölkopf, B., Smola, A.: Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press, Cambridge (2001)
Yang, Z., Guo, J., Xu, W., Nie, X., Wang, J., Lei, J.: Multi-scale Support Vector Machine for Regression Estimation. In: Wang, J., Yi, Z., Zurada, J.M., Lu, B.-L., Yin, H. (eds.) ISNN 2006. LNCS, vol. 3971, pp. 1030–1037. Springer, Heidelberg (2006). https://doi.org/10.1007/11759966_151
Sun, G., Ma, P., Ren, J., et al.: A stability constrained adaptive alpha for gravitational search algorithm. Knowl. Based Syst. 139, 200–213 (2018)
Zhang, A., Sun, G., Ren, J., et al.: A dynamic neighborhood learning-based gravitational search algorithm. IEEE Trans. Cybern. 48(1), 436–447 (2018)
Zheng, D., Wang, J., Zhao, Y.: Training sparse MS-SVR with an expectation–maximization algorithm. Neurocomputing 69(13–15), 1659–1664 (2006)
Phienthrakul, T., Kijsirikul, B.: Evolutionary strategies for multi-scale radial basis function kernels in support vector machines. In: Conference on Genetics and Evaluation Computer, pp. 905–911. ACM, Washington, DC (2005)
Han, J., Zhang, D., Cheng, G., et al.: Object detection in optical remote sensing images based on weakly supervised learning and high-level feature learning. IEEE Trans. Geosci. Remote 53(6), 3325–3337 (2015)
Xu, S., Long, W.: Differential evolution algorithm with dynamically adjusting number of subpopulation individuals. J. Comput. Appl. 31(11), 3101–3103 (2011)
Storn, R., Price, K.: Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces. J. Glob. Optim. 11(4), 341–359 (1997)
Peleg, S., Naor, J., Hartley, R.: Multiple resolution texture analysis and classification. IEEE Trans. Pattern. Anal. PAMI 6(4), 518–523 (2009)
Zhang, J., Pan, Y., He, C.: The high spatial resolution remote sensing image classification based on SVM with the multi-source data. In: Symposium on 2005 IEEE International Geoscience and Remote Sensing, pp. 3818–3821 (2005)
Cheng, G., Han, J., Guo, L., et al.: Effective and efficient midlevel visual elements-oriented land-use classification using VHR remote sensing images. IEEE Trans. Geosci. Remote Sens. 53(8), 4238–4249 (2015)
Acknowledgments
This work was supported by the National Natural Science Foundation of China (41471353), the Natural Science Foundation of Shandong Province (ZR201709180096, ZR201702100118), the Fundamental Research Funds for the Central Universities (18CX05030A, 18CX02179A), and the Postdoctoral Application and Research Projects of Qingdao (BY20170204).
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Rong, X., Zhang, A., Sun, G., Huang, H., Ma, P. (2018). High-Resolution Image Classification Using the Dynamic Differential Evolutionary Algorithm Optimized Multi-scale Kernel Support Vector Machine Method. In: Ren, J., et al. Advances in Brain Inspired Cognitive Systems. BICS 2018. Lecture Notes in Computer Science(), vol 10989. Springer, Cham. https://doi.org/10.1007/978-3-030-00563-4_32
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DOI: https://doi.org/10.1007/978-3-030-00563-4_32
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