Name
Title | ||
|---|---|---|
PolSAR Ship Detection Using the Superpixel-Based Neighborhood Polarimetric Covariance Matrices |
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 [N] 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 vSN and vGN are separately built to compute [SN] and [GN]. Experiments performed on the real PolSAR datasets show that, compared to [N], [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. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/LGRS.2021.3090368 | IEEE GEOSCIENCE AND REMOTE SENSING LETTERS |
Keywords | DocType | Volume |
Marine vehicles, Covariance matrices, Clutter, Detectors, Backscatter, Image edge detection, Matrix decomposition, [N], [GN], [SN], polarimetric synthetic aperture radar (PolSAR), polarimetric whitening filter (PWF), ship detection, simple linear iterative clustering (SLIC), Sobel, superpixels | Journal | 19 |
ISSN | Citations | PageRank |
1545-598X | 0 | 0.34 |
References | Authors | |
0 | 8 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tao Zhang | 1 | 422 | 100.57 |
| Yanlei Du | 2 | 1 | 3.39 |
| Zhen Yang | 3 | 45 | 13.51 |
| Sinong Quan | 4 | 1 | 3.74 |
| Tao Liu | 5 | 2 | 2.73 |
| Fengtao Xue | 6 | 0 | 0.34 |
| Zhengzheng Chen | 7 | 0 | 0.34 |
| Jian Yang | 8 | 483 | 64.80 |