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- W3186207382 abstract "In order to detect ships from the imagery of polarimetric synthetic aperture radar (PolSAR), a neighborhood polarimetric covariance matrix (for simplicity, we call it [<inline-formula> <tex-math notation=LaTeX>$N$ </tex-math></inline-formula>] hereinafter) was recently constructed. However, its calculation process is time-consuming and the backscattering heterogeneity near ship edges is also not well considered. For curing these shortcomings, we here propose two novel superpixel-based neighborhood polarimetric covariance matrices. In brief, the first matrix denoted by [SN] uses the simple linear iterative clustering (SLIC) to yield superpixels, whereas in the second matrix denoted by [GN], the gradient operator Sobel is adopted to obtain superpixels. Based on these two different kinds of superpixels, then, two different feature vectors <inline-formula> <tex-math notation=LaTeX>$v_{text {SN}}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation=LaTeX>$v_{text {GN}}$ </tex-math></inline-formula> are separately built to compute [SN] and [GN]. Experiments performed on the real PolSAR datasets show that, compared to [<inline-formula> <tex-math notation=LaTeX>$N$ </tex-math></inline-formula>], [SN] and [GN] can improve the performance of the polarimetric whitening filter (PWF) more significantly and the time consumptions of calculating [SN] and [GN] are both much less." @default.
- W3186207382 created "2021-08-02" @default.
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- W3186207382 date "2022-01-01" @default.
- W3186207382 modified "2023-10-16" @default.
- W3186207382 title "PolSAR Ship Detection Using the Superpixel-Based Neighborhood Polarimetric Covariance Matrices" @default.
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- W3186207382 doi "https://doi.org/10.1109/lgrs.2021.3090368" @default.
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