Institute of Automatic Control and Robotics, Warsaw University of Technology, A. Boboli 8 St., 02-525 Warsaw, Poland.
Sensors (Basel). 2022 Aug 29;22(17):6519. doi: 10.3390/s22176519.
The scarcity of open SAR (Synthetic Aperture Radars) imagery databases (especially the labeled ones) and sparsity of pre-trained neural networks lead to the need for heavy data generation, augmentation, or transfer learning usage. This paper described the characteristics of SAR imagery, the limitations related to it, and a small set of available databases. Comprehensive data augmentation methods for training Neural Networks were presented, and a novel filter-based method was proposed. The new method limits the effect of the speckle noise, which is very high-level in SAR imagery. The improvement in the dataset could be clearly registered in the loss value functions. The main advantage comes from more developed feature detectors for filter-based training, which is shown in the layer-wise feature analysis. The author attached the trained neural networks for open use. This provides quicker CNN-based solutions implementation.
SAR（合成孔径雷达）公开图像数据库（尤其是带标签的）稀缺，以及预训练神经网络的稀缺，导致需要大量的数据生成、扩充或迁移学习使用。本文描述了 SAR 图像的特点、相关限制以及少量可用数据库。为训练神经网络提出了全面的数据扩充方法，并提出了一种新的基于滤波器的方法。该新方法限制了 SAR 图像中非常高水平的斑点噪声的影响。在损失值函数中可以清楚地记录数据集的改进。主要优势来自于基于滤波器训练的更发达的特征检测器，这在分层特征分析中得到了体现。作者附上了可公开使用的训练后的神经网络。这提供了更快的基于 CNN 的解决方案实现。
