Tian Sirui, Lin Yiyu, Gao Wenyun, Zhang Hong, Wang Chao
Department of Electronic Engineering, School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China.
Department of Electrical and Computer Engineering, University of California, Riverside, Riversidem, CA 92521, USA.
Sensors (Basel). 2020 Mar 10;20(5):1533. doi: 10.3390/s20051533.
Although unsupervised representation learning (RL) can tackle the performance deterioration caused by limited labeled data in synthetic aperture radar (SAR) object classification, the neglected discriminative detailed information and the ignored distinctive characteristics of SAR images can lead to performance degradation. In this paper, an unsupervised multi-scale convolution auto-encoder (MSCAE) was proposed which can simultaneously obtain the global features and local characteristics of targets with its U-shaped architecture and pyramid pooling modules (PPMs). The compact depth-wise separable convolution and the deconvolution counterpart were devised to decrease the trainable parameters. The PPM and the multi-scale feature learning scheme were designed to learn multi-scale features. Prior knowledge of SAR speckle was also embedded in the model. The reconstruction loss of the MSCAE was measured by the structural similarity index metric (SSIM) of the reconstructed data and the images filtered by the improved Lee sigma filter. A speckle suppression restriction was also added in the objective function to guarantee that the speckle suppression procedure would take place in the feature learning stage. Experimental results with the MSTAR dataset under the standard operating condition and several extended operating conditions demonstrated the effectiveness of the proposed model in SAR object classification tasks.
尽管无监督表征学习（RL）能够解决合成孔径雷达（SAR）目标分类中因标记数据有限而导致的性能下降问题，但被忽视的判别性详细信息以及SAR图像中被忽略的独特特征可能会导致性能退化。本文提出了一种无监督多尺度卷积自动编码器（MSCAE），它能够通过其U形架构和金字塔池化模块（PPM）同时获取目标的全局特征和局部特征。设计了紧凑的深度可分离卷积及其反卷积对应部分，以减少可训练参数。PPM和多尺度特征学习方案旨在学习多尺度特征。SAR斑点的先验知识也被嵌入到模型中。MSCAE的重建损失通过重建数据与经改进的Lee sigma滤波器滤波后的图像的结构相似性指数度量（SSIM）来衡量。在目标函数中还添加了斑点抑制约束，以确保在特征学习阶段进行斑点抑制过程。在标准操作条件和几种扩展操作条件下使用MSTAR数据集进行的实验结果证明了所提出模型在SAR目标分类任务中的有效性。
