In response to the needs of maritime target monitoring, combined with the practical application of Synthetic Aperture Radar (SAR), an anchor free SAR image ship target detection model (AF-YOLO) based on YOLO under the premise of sea-land segmentation is proposed. Sea-land segmentation based on the Otsu can remove interference from the terrestrial environment and improve the identification of ships. The detection head based on the anchor free is applied to YOLO, and the weightable feature fusion structure is used for multi-scale fusion. Experiments have shown that the mAP of the proposed algorithm on the public SAR ship data set has reached 93.4%.
In this paper, a grayscale wavelet moment and entropy combined feature, which can well represent the images with much
lower dimensions, is proposed for the MSTAR targets' classification, also a discriminative feature selection and
evaluation method for multi-class targets is presented. By introducing SVM as the classifier to some simulation tests, it
can be shown that the grayscale wavelet moment and entropy combined feature has good capability for translation,
scaling and rotation transformation in both local information and global information conditions, by the reasons of
wavelet moments' multi-resolution analysis, moment invariant quality and entropy's statistic quality for image disorder.
The test also confirms that this feature has a significant improvement on classificatory accuracy using SVM with a lower
feature dimension than the other features such as the single wavelet moment, Hu's moment and PCA.
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