Adversarial Deep Domain Adaptation for Multi-Band SAR Images Classification

Abstract

Deep convolutional neural networks (CNNs) have made a breakthrough on supervised SAR images classification. However, SAR imaging is considerably affected by the frequency band. That means a neural network trained on a SAR image set of one band is not suitable for the classification of another band images. As manually labeling the training samples of each band is always time-consuming, we propose an unsupervised multi-level domain adaptation method based on adversarial learning to solve the problem of multi-band SAR images classification. First, we train a discriminative CNN using samples of one frequency band data set that contains labels to map the data to a latent feature space. Then, we adjust the trained CNN to map the unlabeled samples of another frequency band data set to the same feature space through alternately optimizing two adversarial loss functions. Thus, the features of these two band images are fused and can be classified by the same classifier. We checked the performance of our method using both simulated data and measured data. Our method made a breakthrough in the classification of multi-band images with accuracies of 99% on both data sets. The results are even very close to the supervised CNN trained using a large number of labeled samples.