Synthetic aperture radar (SAR) image classification using deep neural network has drawn great attention, which generally requires various layers of deep model for feature learning. However, a deeper neural network will result in overfitting with limited training samples. In this paper, a multi-scale deep feature learning network with bilateral filtering (MDFLN-BF) is proposed for SAR image classification, which aims to extract discriminative features and reduce the requirement of labeled samples. In the proposed framework, MDFLN is proposed to extract features from SAR image on multiple scales, where the SAR image is stratified into different scales and a full convolutional network is utilized to extract features from each scale sub-image. Then, features of multiple scales are classified by multiple softmax classifiers and combined by majority vote algorithm. Further, bilateral filtering is developed to optimize the classification map based on spatial relation, which aims to improve the spatial smoothness. Experiments are tested on three SAR images with different sensors, bands, resolutions, and polarizations in order to prove the generalization ability. It is demonstrated that the proposed MDFLN-BF is able to yield superior results than other related deep networks.

使用深度神经网络的合成孔径雷达（SAR）图像分类引起了极大的关注，这通常需要多层深度模型进行特征学习。但是，更深的神经网络将导致有限的训练样本过度拟合。本文提出了一种带有双边滤波的多尺度深度特征学习网络（MDFLN-BF），用于SAR图像分类，目的是提取识别特征并减少标记样本的需求。在提出的框架中，提出了MDFLN来从多个尺度的SAR图像中提取特征，其中SAR图像被分层为不同的尺度，并利用一个完整的卷积网络从每个尺度的子图像中提取特征。然后，通过多个softmax分类器对多个尺度的特征进行分类，并通过多数投票算法进行组合。此外，为了改善空间平滑度，开发了双边滤波以基于空间关系优化分类图。为了验证泛化能力，对三个具有不同传感器，波段，分辨率和极化的SAR图像进行了实验测试。结果表明，提出的MDFLN-BF能够比其他相关的深层网络产生更好的结果。