Inverse Synthetic Aperture Radar (ISAR) images of complex targets have a low Signal-to-Noise Ratio (SNR) and contain fuzzy edges and large differences in scattering intensity, which limits the recognition performance of ISAR systems. Also, data scarcity poses a greater challenge to the accurate recognition of components. To address the issues of component recognition in complex ISAR targets, this paper adopts semantic segmentation and proposes a few-shot semantic segmentation framework fusing multimodal features. The scarcity of available data is mitigated by using a two-branch scattering feature encoding structure. Then, the high-resolution features are obtained by fusing the ISAR image texture features and scattering quantization information of complex-valued echoes, thereby achieving significantly higher structural adaptability. Meanwhile, the scattering trait enhancement module and the statistical quantification module are designed. The edge texture is enhanced based on the scatter quantization property, which alleviates the segmentation challenge of edge blurring under low SNR conditions. The coupling of query/support samples is enhanced through four-dimensional convolution. Additionally, to overcome fusion challenges caused by information differences, multimodal feature fusion is guided by equilibrium comprehension loss. In this way, the performance potential of the fusion framework is fully unleashed, and the decision risk is effectively reduced. Experiments demonstrate the great advantages of the proposed framework in multimodal feature fusion, and it still exhibits great component segmentation capability under low SNR/edge blurring conditions.

The continuous emergence of new targets in open scenarios leads to a substantial decrease in the performance of Inverse Synthetic Aperture Radar (ISAR) recognition systems. Also, data scarcity further exacerbates the challenge of identifying new classes of ISAR targets. In this paper, a few-shot incremental target recognition framework based on Scattering-Topology Properties (STPIL) is proposed. Specifically, STPIL extracts scattering-topology properties of ISAR targets as recognition features. Meanwhile, the pseudo-incremental training strategy effectively alleviates the algorithm’s forgetting of old knowledge, and improves compatibility with new classes. Besides, a feature embedding network, with few parameters, is designed based on the graph neural network. This embedding network is highly adaptable to changes in data distribution. Additionally, STPIL fully considers the joint distribution and marginal distribution in scattering features, and uses the Brownian distance metric module to make the scattering-topology features more discriminative. Experimental results on both the simulation dataset and the public measured data indicate that STPIL can effectively balance new classes with old classes, and has superior performance to other advanced methods in the incremental recognition of targets.