Although numerous methods based on sequence image classification have improved the accuracy of synthetic-aperture radar (SAR) automatic target recognition, most of them only concentrate on the fusion of spatial features of multiple images and fail to fully utilize the temporal-varying features. In order to exploit the spatial and temporal features contained in the SAR image sequence simultaneously, this article proposes a sequence SAR target classification method based on the spatial–temporal ensemble convolutional network (STEC-Net). In the STEC-Net, the dilated 3-D convolution is first applied to extract the spatial–temporal features. Then, the features are gradually integrated hierarchically from local to global and represented as the united tensors. Finally, a compact connection is applied to obtain a lightweight classification network. Compared with the available methods, the STEC-Net achieves a higher accuracy (99.93%) in the moving and stationary target acquisition and recognition (MSTAR) data set and exhibits robustness to depression angle, configuration, and version variants.

中文翻译：

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序列 SAR 目标分类的时空集成卷积

尽管众多基于序列图像分类的方法提高了合成孔径雷达（SAR）自动目标识别的准确率，但大多只专注于多幅图像空间特征的融合，未能充分利用随时间变化的特征。为了同时利用SAR图像序列中包含的空间和时间特征，本文提出了一种基于时空集成卷积网络（STEC-Net）的序列SAR目标分类方法。在 STEC-Net 中，首先应用扩张的 3-D 卷积来提取时空特征。然后，特征从局部到全局逐渐分层集成，并表示为联合张量。最后，应用紧凑连接来获得轻量级分类网络。