Unlocking Optical Prior: Spectrum-Guided Knowledge Transfer for SAR Generalized Category Discovery

TL;DR

This paper introduces a frequency-domain discrepancy modeling approach, MDC, to transfer optical prior knowledge into SAR imagery, significantly improving generalized category discovery in label-scarce SAR data.

Contribution

The paper proposes the MDC-guided transfer framework with adaptive frequency tokenization and frequency-aware refinement, enabling effective optical prior transfer to SAR imagery.

Findings

Achieves state-of-the-art results on multiple SAR datasets.

Effectively models cross-modal discrepancy in the frequency domain.

Enhances SAR feature representation for better category discovery.

Abstract

Generalized Category Discovery (GCD) holds significant promise for the label-scarce Synthetic Aperture Radar (SAR) domain, yet its efficacy is severely constrained by the cross-modal incompatibility between the inherent optical prior of the Large Vision Models (LVMs) and SAR imagery. Existing domain adaptation methods often lack an inductive bias that reflects imaging characteristics, consequently failing to effectively transfer optical prior into the SAR domain. To address this issue, the Modal Discrepancy Curve (MDC) is introduced to model cross-modal discrepancy as a structured frequency-domain descriptor derived from spectral energy distributions. Leveraging this formulation, we propose the MDC-guided Cross-modal Prior Transfer (MCPT) framework, a pre-training paradigm that operates on paired optical-SAR data. Within this framework, Adaptive Frequency Tokenization (AFT) converts the MDC into learnable tokens, and Frequency-aware Expert Refinement (FER) performs band-wise discrepancy-aware feature refinement using these tokens. Based on the refined representations, contrastive learning aligns refined embeddings across modalities and internalizes the adaptation pattern. Ultimately, the superior SAR feature representation capability learned during paired pre-training is applied to downstream single-modal SAR-GCD tasks. Extensive experiments demonstrate state-of-the-art performance across multiple mainstream datasets, indicating that frequency-domain discrepancy modeling enables more effective adaptation of optical prior to SAR imagery.