A Few-Shot Method Based on Spatial-Frequency Dual-Domain Feature Enhancement and Hypersphere Mapping Constraint for SAR Ship Recognition

Synthetic aperture radar (SAR) can acquire maritime imagery independently of daylight and under diverse weather conditions, which makes it well suited to persistent ship monitoring and maritime awareness. Nevertheless, recognizing SAR ships from only a few labeled examples remains difficult for several reasons. First, sea clutter and speckle noise may weaken ship-related structural and scattering-response information. Second, features from different categories may become difficult to separate because between-class differences are small while within-class variations remain substantial. Third, when each category contains only a few support examples, an abnormal or noisy sample can noticeably distort the estimated prototype. To address these issues, a few-shot SAR ship recognition method based on spatial-frequency dual-domain feature enhancement and hypersphere mapping constraint (SFHMC) is proposed. First, the spatial-frequency feature enhancement and extraction module (SFEE) combines the Haar discrete wavelet transform with multi-scale spatial modeling. It enhances structural, textural, and scattering-response features under complex backgrounds. Second, the hypersphere mapping constraint module (HMCM) normalizes support and query embeddings onto a unit hypersphere. By removing differences in feature magnitude, this mapping makes similarity estimation depend mainly on feature direction and produces a more structured class distribution. Third, the sample-weighted prototype generation module (SWPGM) adaptively adjusts the contribution of each support sample according to its consistency with the initial class prototype. This reduces prototype bias caused by noisy or atypical samples. The three modules jointly optimize feature representation, metric discrimination, and prototype calibration. Experiments are conducted on FUSAR-Ship and OpenSARShip, with additional validation on MSTAR under 3-way 1-shot and 3-way 5-shot settings. Compared with MBEN-BC, SFHMC improves classification accuracy by 1.41 and 1.96 percentage points on FUSAR-Ship, 2.68 and 4.46 percentage points on OpenSARShip, and 6.40 and 0.48 percentage points on MSTAR, respectively. These results demonstrate the effectiveness of SFHMC for few-shot SAR target recognition under limited data and complex imaging conditions.