Active Learning SAR Image Classification Method Crossing Different Imaging Platforms

A synthetic aperture radar (SAR) image classification task when the training and test sets have different distributions can be initially solved using the existing domain adaptation (DA) methods. However, considering that none of their classification accuracy is high, this letter proposes an active learning DA classification method to further solve this task. First, an adversarial learning-based DA pipeline is put forth, using labeled source and unlabeled target domains to conduct adversarial learning to narrow the domain gap. A prototype regularization process is then built, which further enhances the target domain data clusters' ability to discriminate between them. To fully improve the SAR image classification accuracy, we then propose a dynamic hard sample selection process to choose hard samples to supplement into the subsequent stage of training samples. This process involves moving the gradient direction of the query function closer to the gradient direction of the class margin objective function. Extensive experiments on SAR image datasets with different distributions from different imaging platforms and optical remote sensing datasets have verified the effectiveness and superiority of the proposed method.