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首页> 外文期刊>IEEE Geoscience and Remote Sensing Letters >SAR Target Small Sample Recognition Based on CNN Cascaded Features and AdaBoost Rotation Forest
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SAR Target Small Sample Recognition Based on CNN Cascaded Features and AdaBoost Rotation Forest

机译：基于CNN级联特征和Adaboost旋转林的SAR目标小样本识别

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摘要

Automatic target recognition (ATR) has made great progress with the development of deep learning. However, the target feature in synthetic aperture radar (SAR) image is not consistent with human vision, and the SAR training samples are always limited. These hard issues pose new challenges to the SAR ATR based on convolutional neural network (CNN). In this letter, we propose an improved CNN model to solve the limited sample issue via the feature augmentation and ensemble learning strategies. Normally, the high-level features that are more comprehensive and discriminative than the middle-level and low-level features are always employed for category discrimination. In order to make up the insufficient training features in the limited sample case, the cascaded features from optimally selected convolutional layers are concatenated to provide more comprehensive representation for the recognition. To take full advantage of these cascaded features, the ensemble learning-based classifier, namely, the AdaBoost rotation forest (RoF), is introduced to replace the original softmax layer to realize a more accurate limited sample recognition. Through the AdaBoost RoF method, not only are these features further enhanced by the rotation matrix but also a strong classifier is constructed by several weak classifiers with different adjusted weights. The experimental results on MSTAR data set show that the cascaded features and ensemble weak classifiers can fully exploit effective information in limited samples. Compared with the existing CNN method, the proposed method can improve the recognition accuracy by about 20% under the condition of ten training samples per class.
机译：随着深度学习的发展，自动目标识别（ATR）取得了巨大进展。然而，合成孔径雷达（SAR）图像中的目标特征与人类视觉不一致，并且SAR训练样品总是有限的。这些艰难的问题基于卷积神经网络（CNN）对SAR ATR构成了新的挑战。在这封信中，我们提出了一种改进的CNN模型，通过功能增强和集合学习策略来解决有限的示例问题。通常，始终采用更全面和低级功能的高级别特征，以用于类别辨别。为了构成有限的样品盒中的训练特征不足，从最佳选择的卷积层的级联特征被连接，以提供更全面的识别表示。为了充分利用这些级联特征，介绍了基于集群的基于学习的分类器，即adaboost旋转森林（ROF），以取代原始的Softmax层来实现更准确的有限样本识别。通过Adaboost Rof方法，不仅通过旋转矩阵进一步增强的这些特征，而且还由具有不同调整的权重的几个弱分类器构成强分类器。 MSTAR数据集的实验结果表明，级联功能和集合弱分类器可以充分利用有限的样品中的有效信息。与现有的CNN方法相比，所提出的方法可以在每阶级十个训练样本的条件下提高识别精度约20％。

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