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Automatic target recognition is a crucial task for SAR remote sensing. Unlike other methods, the unsupervised representation learning based on deep architecture can obtain robust high-level features directly from raw data. A drawback of most unsupervised representation learning methods in SAR ATR is that they only deal with amplitude images. In addition, many methods utlize a single layer architecture...
This letter depicts a ship detection scheme for synthetic aperture radar images, utilizing a segmentation based global iterative censoring algorithm. In the proposed scheme, the fuzzy local information c-means clustering (RFLICM) algorithm is adopted to partition the inhomogeneous SAR image into numerous homogeneous sub-regions, thereby eliminating the performance degradation caused by SAR image inhomogeneity...
In this letter, an improved kernel density estimation (KDE) constant false alarm rate (CFAR) method is proposed for ship detection in single polarization synthetic aperture radar (SAR) images. The proposed method consists of a target enhancement filter, an adaptive KDE bandwidth estimation method and an improved KDE-CFAR. The gravity-based target enhancement filter is utilized to remove the inhomogeneity...
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