The limitations of existing Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR) methods lie in their confinement by the closed-environment assumption, hindering their effective and robust handling of unknown target categories in open environments. Open Set Recognition (OSR), a pivotal facet for algorithmic practicality, intends to categorize known classes while denoting unknown ones as "unknown." The chief challenge in OSR involves concurrently mitigating risks associated with generalizing features from a restricted set of known classes to numerous unknown samples and the open space exposure to potential unknown data. To enhance open-set SAR classification, a method called scattering kernel with reciprocal learning network is proposed. Initially, a feature learning framework is constructed based on reciprocal point learning (RPL), establishing a bounded space for potential unknown classes. This approach indirectly introduces unknown information into a learner confined to known classes, thereby acquiring more concise and discriminative representations. Subsequently, considering the variability in the imaging of targets at different angles and the discreteness of components in SAR images, a proposal is made to design convolutional kernels based on large-sized attribute scattering center models. This enhances the ability to extract intrinsic non-linear features and specific scattering characteristics in SAR images, thereby improving the discriminative features of the model and mitigating the impact of imaging variations on classification performance. Experiments on the MSTAR datasets substantiate the superior performance of the proposed approach called ASC-RPL over mainstream methods.

翻译：现有合成孔径雷达（SAR）自动目标识别（ATR）方法的局限性在于其受限于封闭环境假设，阻碍了其在开放环境中对未知目标类别的有效且鲁棒的处理。开放集识别（OSR）作为算法实用性的关键环节，旨在对已知类别进行分类，同时将未知类别标记为“未知”。OSR的主要挑战在于同时缓解以下两个风险：一是将有限已知类别的特征泛化至大量未知样本所带来的风险，二是开放空间暴露于潜在未知数据的风险。为提升开放集SAR分类性能，本文提出了一种基于散射核与互易学习网络的方法。首先，基于互易点学习（RPL）构建特征学习框架，为潜在未知类别建立有界空间。该方法将未知信息间接引入仅限于已知类别的学习器中，从而获得更简洁且更具判别性的特征表示。其次，考虑到目标在不同角度成像的变异性以及SAR图像中散射成分的离散性，本文提出基于大尺寸属性散射中心模型设计卷积核。这增强了从SAR图像中提取固有非线性特征与特定散射特性的能力，从而提升模型的判别性特征并减轻成像变化对分类性能的影响。在MSTAR数据集上的实验验证了所提方法（称为ASC-RPL）相较于主流方法的优越性能。