PAD: Phase-Amplitude Decoupling Fusion for Multi-Modal Land Cover Classification
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.
Code (1)
Tasks
Land Cover ClassificationSimilar Papers 제목 키워드 기반
PAMNet: Cycle-aware Phase-Amplitude Modulation Network for Multivariate Time Series Forecasting
Reliable periodic patterns serve as a fundamental basis for accurate multivariate time series forecasting. However, existing methods either implicitly extract periodicity through complex model architectures (e.g., Transf…
Multivariate Time Series ForecastingDecoupling Amplitude and Phase Attention in Frequency Domain for RGB-Event based Visual Object Tracking
Existing RGB-Event visual object tracking approaches primarily rely on conventional feature-level fusion, failing to fully exploit the unique advantages of event cameras. In particular, the high dynamic range and motion-…
Visual Object TrackingBeyond Amplitude: Channel State Information Phase-Aware Deep Fusion for Robotic Activity Recognition
Wi-Fi Channel State Information (CSI) has emerged as a promising non-line-of-sight sensing modality for human and robotic activity recognition. However, prior work has predominantly relied on CSI amplitude while underuti…
Activity RecognitionGeneralizable Holographic Reconstruction via Amplitude-Only Diffusion Priors
Phase retrieval in inline holography is a fundamental yet ill-posed inverse problem due to the nonlinear coupling between amplitude and phase in coherent imaging. We present a novel off-the-shelf solution that leverages …
Stationarity Exploration for Multivariate Time Series Forecasting
Deep learning-based time series forecasting has found widespread applications. Recently, converting time series data into the frequency domain for forecasting has become popular for accurately exploring periodic patterns…
Multivariate Time Series Forecasting