PAD: Phase-Amplitude Decoupling Fusion for Multi-Modal Land Cover Classification
Journal:
arXiv
Published Date:
Apr 27, 2025
Abstract
The fusion of Synthetic Aperture Radar (SAR) and RGB imagery for land cover
classification remains challenging due to modality heterogeneity and the
underutilization of spectral complementarity. Existing methods often fail to
decouple shared structural features from modality-specific radiometric
attributes, leading to feature conflicts and information loss. To address this
issue, we propose Phase-Amplitude Decoupling (PAD), a frequency-aware framework
that separates phase (modality-shared) and amplitude (modality-specific)
components in the Fourier domain. Specifically, PAD consists of two key
components: 1) Phase Spectrum Correction (PSC), which aligns cross-modal phase
features through convolution-guided scaling to enhance geometric consistency,
and 2) Amplitude Spectrum Fusion (ASF), which dynamically integrates
high-frequency details and low-frequency structures using frequency-adaptive
multilayer perceptrons. This approach leverages SAR's sensitivity to
morphological features and RGB's spectral richness. Extensive experiments on
WHU-OPT-SAR and DDHR-SK datasets demonstrate state-of-the-art performance. Our
work establishes a new paradigm for physics-aware multi-modal fusion in remote
sensing. The code will be available at https://github.com/RanFeng2/PAD.
