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Abstract
In this article, a learning-based target decomposition method based on Kernel K-singular vector decomposition (Kernel KSVD) algorithm is proposed for polarimetric synthetic aperture radar (PolSAR) image classification. With new methods offering increased resolution, more details (structures and objects) could be exploited in the SAR images, thus invalidating the traditional decompositions based on specific scattering mechanisms offering low-resolution SAR image classification. Instead of adopting fixed bases corresponding to the known scattering mechanisms, we propose a learning-based decomposition method for generating adaptive bases and developing a nonlinear extension of the KSVD algorithm in a nonlinear feature space, called as Kernel KSVD. It is an iterative method that alternates between sparse coding in the kernel feature space based on the nonlinear dictionary and a process of updating each atom in the dictionary. The Kernel KSVD-based decomposition not only generates a stable and adaptive representation of the images but also establishes a curvilinear coordinate that goes along the flow of nonlinear polarimetric features. This proposed approach was verified on two sets of SAR data and found to outperform traditional decompositions based on scattering mechanisms.
Highlights
Synthetic Aperture Radar (SAR)[1] has become an important tool for a wide range of applications, including in military exploration, resource exploration, urban development planning and marine research
We describe the current target decompositions based on scattering mechanisms in Section “Target decomposition based on scattering mechanisms” and present the framework of our proposed Kernel KSVD algorithm for the learning-based decomposition in Section “A novel learning-based target decomposition method based on Kernel KSVD for polarimetric SAR (PolSAR) image”
Kernel KSVD can always show an acceptable accuracy for different ground objects without considering this relationship mango3 betelnut longan due to its adaptive learning-based method
Summary
Synthetic Aperture Radar (SAR)[1] has become an important tool for a wide range of applications, including in military exploration, resource exploration, urban development planning and marine research. The coherent decompositions express the measured scattering matrix by radar as a combination of simpler responses, mainly as the Pauli, the Krogager and the Cameron decompositions. These decompositions are possible only if the scatters are points or pure targets. Incoherent decompositions deal with polarimetric coherency matrix or covariance matrix, such as the Freeman, the OEC, the FourComponent, the Huynen, the Barnes and the Cloude decompositions. These traditional methods aim to associate each decomposition component with a specific scattering mechanism, invalidating their applications for different kinds of PolSAR images. With improved resolution of SAR images, targets in the images become clearer and clearer, and a pixel no longer purely consists of several kinds of scattering mechanisms—the limited scattering mechanisms being explored currently may be unable to satisfy pluralism
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