Freq-RemoteVAR: Next-Frequency Autoregressive Modeling for Remote Sensing Change Detection

2026-07-28

AI Summary

This work addresses the limitations of existing remote sensing change detection methods, which typically rely on one-shot dense prediction and overlook the frequency characteristics of changes, leading to poor robustness under complex appearance variations and noise. To overcome this, the paper reformulates change detection as a structured generative task in the frequency domain and introduces a next-frequency autoregressive modeling paradigm that progressively generates change maps from coarse to fine scales. Key innovations include a frequency-aware masked tokenization strategy, a scale-aligned RoPE cross-attention module, and a change quality control mechanism. Built upon Fourier transform and quantized decomposition, the proposed Frequency VAR Transformer integrates dynamic normalization with spatial-frequency alignment. Extensive experiments on CDD, GZ-CD, and LEVIR-CD benchmarks demonstrate significant performance gains over state-of-the-art methods, particularly exhibiting superior robustness in complex scenarios.

Abstract

Remote sensing change detection aims to identify land-cover changes from bi-temporal images. Most existing methods follow a one-shot dense prediction paradigm, directly regressing a change mask from fused features. However, such approaches overlook the intrinsic frequency characteristics of change patterns. We propose Freq-RemoteVAR, a frequency autoregressive framework that reformulates change detection as a structured generation problem in the frequency domain. Instead of predicting the change mask in a single step, we introduce a next-frequency prediction paradigm, where change information is progressively generated from coarse to fine. We design a frequency-aware mask tokenization strategy that decomposes change supervision into multi-frequency token targets via Fourier transformation and quantization. We develop a Frequency VAR Transformer, which performs causal autoregressive modeling over frequency tokens. The model starts from learned mask queries and progressively predicts frequency-level tokens conditioned on previously generated tokens and bi-temporal image features, effectively capturing long-range dependencies across frequency scales. We introduce Scale-Aligned RoPE Cross Attention (SRCA) module, which aligns frequency-domain mask queries with spatial-domain bi-temporal features under a unified coordinate system, enhancing spatial-frequency consistency during generation. We propose a Change-quality Control module that adaptively modulates the generation process through dynamic normalization, attention biasing, and spatial offset adjustment, thereby suppressing pseudo-change responses and improving robustness. Extensive experiments on CDD, GZ-CD, and LEVIR-CD demonstrate that Freq-RemoteVAR consistently outperforms existing methods, particularly in challenging scenarios with complex appearance variations and noisy disturbances.

Problem

• remote sensing change detection
• frequency characteristics
• change mask prediction
• bi-temporal images
• pseudo-change suppression

Innovation

• frequency autoregressive modeling
• remote sensing change detection
• Fourier tokenization
• Frequency VAR Transformer
• spatial-frequency alignment