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首页> 外文期刊>IEEE Transactions on Geoscience and Remote Sensing >FEC: A Feature Fusion Framework for SAR Target Recognition Based on Electromagnetic Scattering Features and Deep CNN Features
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FEC: A Feature Fusion Framework for SAR Target Recognition Based on Electromagnetic Scattering Features and Deep CNN Features

机译：FEC：基于电磁散射特征和深层CNN特征的SAR目标识别特征融合框架

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摘要

The active recognition of interesting targets has been a vital issue for synthetic aperture radar (SAR) systems. The SAR recognition methods are mainly grouped as follows: extracting image features from the target amplitude image or matching the testing samples with the template ones according to the scattering centers extracted from the target complex data. For amplitude image-based methods, convolutional neural networks (CNNs) achieve nearly the highest accuracy for images acquired under standard operating conditions (SOCs), while scattering center feature-based methods achieve steady performance for images acquired under extended operating conditions (EOCs). To achieve target recognition with good performance under both SOCs and EOCs, a feature fusion framework (FEC) based on scattering center features and deep CNN features is proposed for the first time. For the scattering center features, we first extract the attributed scattering centers (ASCs) from the input SAR complex data, then we construct a bag of visual words from these scattering centers, and finally, we transform the extracted parameter sets into feature vectors with the k-means. For the CNN, we propose a modified VGGNet, which can not only extract powerful features from amplitude images but also achieve state-of-the-art recognition accuracy. For the feature fusion, discrimination correlation analysis (DCA) is introduced to the FEC framework, which not only maximizes the correlation between the CNN and ASCs but also decorrelates the features belonging to different categories within each feature set. Experiments on Moving and Stationary Target Acquisition and Recognition (MSTAR) database demonstrate that the proposed FEC achieves superior effectiveness and robustness under both SOCs and EOCs.
机译：对有趣的目标的积极识别对于合成孔径雷达（SAR）系统来说是一个重要问题。 SAR识别方法主要分组如下：从目标幅度图像提取图像特征或根据从目标复杂数据中提取的散射中心与模板的图像特征匹配。对于基于幅度图像的方法，卷积神经网络（CNNS）实现在标准操作条件（SOC）下获取的图像的几乎最高精度，而基于散射中心特征的方法可以实现在扩展操作条件（EoC）下获取的图像的稳定性能。为了在SOC和EOC下实现具有良好性能的目标识别，第一次提出了一种基于散射中心特征和深CNN特征的特征融合框架（FEC）。对于散射中心功能，我们首先从输入SAR复杂数据中提取属性散射中心（ASCS），然后我们构建了这些散射中心的一袋视觉单词，最后，我们将提取的参数集转换为具有的特征向量k均值。对于CNN，我们提出了一种修改的VGGNet，它不仅可以从幅度图像中提取强大的特征，而且可以实现最先进的识别精度。对于特征融合，将判别相关性分析（DCA）引入FEC框架，其不仅最大化CNN和ASC之间的相关性，而且还将属于每个特征集内的不同类别的特征消除。移动和静止目标采集和识别（MSTAR）数据库的实验表明，建议的FEC在SOC和EOC中实现了卓越的效力和鲁棒性。

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