Abstract
To improve polarimetric synthetic aperture radar (PolSAR) imagery segmentation accuracy, a modified non-negative matrix factorization algorithm based on the support vector machine is proposed. Focusing on PolSAR remote sensing images, the modified non-negative matrix factorization algorithm with the neurodynamic optimization achieves the image feature extraction. Compared with basic features, such as the basic backscatter coefficient, structuring more targeted localization non-negative character fits better for the physical significance of remote sensing images. Furthermore, based on the new constructive features, a support vector machine is employed for remote sensing image classification, which remedies the small sample training problem. Simulation results on PolSAR image classification substantiate the effectiveness of the proposed approach.
This research was supported by the project (61273307) of the National Nature Science Foundation of China, and supported by the Foundation (201313) of Key Laboratory of Marine Spill Oil Identification and Damage Assessment Technology and also supported by China Postdoctoral Science Foundation (2014M551082). The work described in the chapter was also supported by the Research Grants Council of the Hong Kong Special Administrative Region, China, under Grants CUHK416812E.
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Fan, J., Wang, J., Zhao, D. (2014). PolSAR Image Segmentation Based on the Modified Non-negative Matrix Factorization and Support Vector Machine. In: Zeng, Z., Li, Y., King, I. (eds) Advances in Neural Networks – ISNN 2014. ISNN 2014. Lecture Notes in Computer Science(), vol 8866. Springer, Cham. https://doi.org/10.1007/978-3-319-12436-0_66
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DOI: https://doi.org/10.1007/978-3-319-12436-0_66
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