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InSAR Patch Categorization Using Sparse Coding
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DOI：10.1109/LGRS.2017.2689506.png)
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Abstract
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This letter presents sparse coding for interferometric synthetic aperture radar (InSAR) patch categorization. Motivated by the fact that an optimal dual based l(1) analysis can achieve better recognition rates, this letter proposes sparse coding with optimal dual-based l(1) analysis, which is applied to the amplitude and phase of the InSAR patches. The minimization of cost functions for amplitude and phase was designed and solved differently. The cost function for the amplitude part of InSAR data was modeled using the optimal dual-based l(1) analysis, and the minimization of cost function was solved using the forward-backward splitting algorithm. The phase was coded sparsely using the l(1) minimization approach and it was solved using the gradient descent algorithm. The experimental results showed that the proposed method outperforms the complex-valued methods for SAR patch categorization and outperforms the bag of visual words method as well.
Keywords:
Image classification
object detection
SAR patch categorization
sparse coding
synthetic aperture radar (SAR)
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