151. A Novel Polarization Scattering Decomposition Model and Its Application to Ship Detection.
- Author
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Fang, Lu, Yang, Ziyuan, Mu, Wenxing, and Liu, Tao
- Subjects
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SYNTHETIC aperture radar , *MACHINE learning , *SYNTHETIC apertures , *DECOMPOSITION method , *RADARSAT satellites , *SUPPORT vector machines , *SURFACE scattering - Abstract
In polarimetric synthetic aperture radar (POLSAR), it is of great significance for civil and military applications to find novel model-based decomposition methods suitable for ship detection in different detection backgrounds. Based on the physical interpretation of polarimetric decomposition theory and the Lasso rule for sparse features, we propose a four-component decomposition model, which is composed of surface scattering (Odd), double-bounce scattering (Dbl), volume scattering (Vol), and ±45° oriented dipole (Od). In principle, the Od component can describe the compounded scattering structure of a ship consisting of odd-bounce and even-bounce reflectors. Moreover, the pocket perceptron learning algorithm (PPLA) and support vector machine (SVM) are utilized to solve the linear inseparable problems in this study. Using large amounts of RADARSAT-2 (RS-2) fully polarized SAR data and AIRSAR data, our experimental results show that the Od component can make a great contribution to ship detection. Compared with other conventional decomposition methods used in the experiments, the proposed four-component decomposition method has better performance and is more effective and feasible to detect ships. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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