Zeping Li, Hongru Wang, Yiwen Zhao, Guanhua Chen, Yixia Li, Keyang Chen, Yixin Cao, Guangnan Ye, Hongfeng Chai, Zhenfei Yin. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

The traditional CNN-based methods usually employ the spatial information in the amplitude of complex Synthetic Aperture Radar (SAR) images. Several studies have started to concentrate on merging the unique physical properties of SAR images, such as DSN-v1, extracting the backscattering characteristic from the frequency domain. Although DSN-v1 has obtained impressive classification ability, there is some room for improvement. In this letter, DSN-v2 is proposed to boost the classification ability of man-made and natural objects in SAR images. The improvement is reflected in two aspects. First, a multi-scale sub-band feature extraction (MSFE) component is designed for natural objects. Since we observe their multi-scale sub-band spectrum is significantly different, multiple encoders are used to extract effective features. Second, the additive angular margin (AAM) loss is introduced to distinguish man-made objects more clearly by manually adding a margin to the decision boundary. The experimental results on the Sentinel-1 (S1) dataset show DSN-v2 achieves superior classification performance and model training speed compared with DSN-v1.

In the practical application of remote sensing scene classification (RSSC), domain adaptation is introduced to handle the situation where the distribution of training data (source) and test data (target) is different. Compared to general domain adaptation, open set domain adaptation (OSDA) is suitable for more realistic situations where there are additional unknown classes in the target domain. The key to solving this problem is to separate unknown samples from target data to avoid negative transfer caused by mismatching unknown/known samples. In this letter, we propose a novel and effective method, named Instance Affinity metric-based Fine-grained Adaptation Network (IAFAN), for OSDA in RSSC. Concretely, an Unknown Sample Separation (USS) mechanism based on the instance affinity-aware matrix is pioneeringly proposed to endow the model with the ability to distinguish unknown samples. In addition, considering the high inter-class similarity and rich intra-class diversity of remote sensing images, we introduce the Sample Discriminability Enhancement (SDE) loss to further increase the inter-class distance and narrow the intra-class difference, thereby alleviating the negative transfer caused by sample misclassification and mismatching. In the feature confusion stage, we specially design Mask-mmd for OSDA as an adaptation metric to conduct semantic fine-grained cross-domain alignment of known samples while keeping unknown samples out of alignment, which avoids negative transfer during the adaptation process. Finally, we evaluate IAFAN on transfer tasks between different public remote sensing datasets, and the results verify that our method significantly outperforms previous methods in RSSC.

Synthetic aperture radar automatic target recognition (SAR ATR) has been suffering from the insufficient labeled samples as the annotation of SAR data is time-consuming. Thus, adding unlabeled samples into training has attracted the attention of researchers. In this letter, a semi-supervised method based on consistency criterion, domain adaptation and Top-k loss is proposed to alleviate the need for labeled samples. According to consistency criterion that samples generated by the weak and strong augmentations from the same sample belong to the same category, we use the weak and strong augmented unlabeled samples to predict pseudo labels and train the model respectively. Then, to overcome the issue caused by the domain discrepancy between labeled and unlabeled samples especially when labeled samples concentrate on a narrow azimuth range, a domain adaptation component is designed to reduce their discrepancy. Besides, considering the incorrect pseudo labels will hamper the model training, the Top-k loss is adopted for unlabeled samples to mitigate the negative effects. The experimental results on Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset demonstrate the superiority of our method in semi-supervised SAR ATR. Specifically, we achieve about a 14.29% improvement in recognition accuracy compared to the state-of-the-art when the labeled samples concentrate on a narrow azimuth range.