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Method and apparatus for SAR image recognition based on multi-scale features and broad learning

机译:基于多尺度特征和广泛学习的SAR图像识别方法和装置

摘要

Disclosed are method and apparatus for SAR image recognition based on multi-scale features and broad learning. A region of interest of an original SAR image is extracted by centroid localization, the image is rotated and added with noise for enhancing the data volume, the image is downsampled, LBP features and PPQ features are extracted, an LBP feature vector XLBP and an LPQ feature vector XLPQ are cascaded to achieve dimension reduction by principal component analysis to obtain a fusion feature data Xm, the fusion feature data Xm is input to a broad learning network for image recognition and a recognition result is output. By fusing the LBP features and the LPQ features, complementary information is fully utilized and redundant information is reduced. The broad learning network is used to improve the training speed and reduce the time cost. As a result, the recognition effect is more stable, robust and reliable.
机译:公开了基于多尺度特征和广泛学习的SAR图像识别的方法和装置。通过质心定位提取原始SAR图像的感兴趣区域,通过质心定位提取并添加噪声以增强数据量的噪声,将图像下采样,提取LBP特征和PPQ特征,LBP特征向量x LBP 和LPQ特征向量x LPQ 通过主成分分析来实现尺寸减小,以获得融合特征数据x m ,该融合特征数据x m 输入到广泛的学习网络以进行图像识别,输出识别结果。通过融合LBP功能和LPQ功能,充分利用了互补信息,并且减少了冗余信息。广泛的学习网络用于提高训练速度并降低时间成本。结果,识别效果更稳定,稳健可靠。

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