Alliance: All-in-One Spectral-Spatial-Frequency Awareness Foundation Model

Frequency domain analysis reveals fundamental image patterns difficult to observe in raw pixel values, while avoiding redundant information in original image processing. Although recent remote sensing foundation models (FMs) have made progress in leveraging spatial and spectral information, they have limitations in fully utilizing frequency characteristics that capture hidden features. Existing FMs that incorporate frequency properties often struggle to maintain connections with the original image content, creating a semantic gap that affects downstream performance. To address these challenges, we propose the All-in-One Spectral-Spatial-Frequency Awareness Foundation Model (Alliance), a framework that effectively integrates information across all three domains. Alliance introduces several key innovations: (1) a progressive frequency decoding mechanism inspired by human visual cognition that minimizes multi-domain information gaps while preserving connections between general image information and frequency characteristics, progressively reconstructing from low to mid to high frequencies to extract patterns difficult to observe in raw pixel values; (2) a triple-domain fusion attention module that separately processes amplitude, phase, and spectral-spatial relationships for comprehensive feature integration; and (3) frequency embedding with frequency-aware Cls token initialization and frequency-specific mask token initialization that achieves fine-grained modeling of different frequency band information. Additionally, to evaluate FMs generalizability, we construct the Yellow River dataset, a large-scale multi-temporal collection that introduces challenging cross-domain tasks and establishes more rigorous standards for FMs assessment. Extensive experiments across six downstream tasks demonstrate Alliance's superior performance.