Target Classification for Single-Channel SAR Images Based on Transfer Learning With Subaperture Decomposition

Abstract

Synthetic aperture radar (SAR) images have limited labeled samples, and thus, it is difficult to learn a perfect convolutional neural network (CNN) model for target classification. The commonly used single-channel SAR images have much less information than those of the three-channel natural images. Transfer learning (TL) is an effective way to improve the generalization ability of the CNN model. The existing TL methods for SAR images usually transfer the knowledge from the three-channel natural images to the single-channel SAR images, where the SAR images are simply duplicated from one channel to three channels. This is obviously not reasonable. Indeed, the single-channel SAR image is complex valued, which can be divided into multiple channels (e.g., three channels) via the subaperture decomposition (SD) algorithm. In order to fully utilize the complex-valued data of the single-channel SAR images, in this letter, we propose a novel TL method with SD (TL-SD), where the SD can generate pseudocolor SAR images to realize TL with the large-scale natural image data sets. The experimental results based on the MSTAR real data set show that the proposed TL-SD method achieves an average accuracy of 99.88% on classification of ten-class targets and is superior to the other compared target classification methods, which verify the effectiveness of the proposed method.