Multi-scale deep feature learning network with bilateral filtering for SAR image classification

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.