Domain-Incremental Remote Sensing Change Detection via Difference-Guided Adaptation and Frequency-Decoupled Distillation

2026-07-14 · Source: Computer Vision and Pattern Recognition · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, quick

Summary

DG-FDD is a novel domain-incremental change detection framework addressing catastrophic forgetting in remote sensing change detection (RSCD) models during adaptation to new domains. Existing methods often overlook bitemporal discrepancy cues critical for robust change detection under domain shifts. DG-FDD integrates a Difference-Guided Dynamic Adapter (DGDA) to model bitemporal feature discrepancies, promoting change-aware adaptation and reducing domain-specific interference. It also uses a Frequency-Decoupled Knowledge Distillation strategy with Cross-domain Synthesis (FDKD-CS) to separate structural information from domain style in the frequency domain, enabling stable knowledge transfer without historical data. Experiments on three public high-resolution RSCD datasets, under two- and three-domain incremental protocols, demonstrate DG-FDD effectively mitigates forgetting. It achieved mean relative changes in F1 and IoU of only -0.23% and -0.45% across six two-domain sequences, and -0.69% and -1.31% across three three-domain sequences, showcasing a strong stability-plasticity balance.

Key takeaway

For Machine Learning Engineers developing remote sensing change detection models in dynamic environments, traditional domain-incremental learning methods often fall short due to catastrophic forgetting. You should consider integrating DG-FDD's approach, which uses difference-guided adaptation and frequency-decoupled distillation. This framework helps maintain high performance, with minimal F1 and IoU degradation (e.g., -0.23% F1, -0.45% IoU for two-domain sequences), ensuring your models adapt to new domains effectively while retaining historical knowledge.

Key insights

DG-FDD mitigates catastrophic forgetting in remote sensing change detection by integrating difference-guided adaptation and frequency-decoupled distillation.

Principles

• Bitemporal discrepancy cues are critical for robust change detection under domain shifts.
• Separating structural information from domain style in the frequency domain aids stable knowledge transfer.
• Balancing historical knowledge retention with new-domain adaptation is key for continual learning.

Method

DG-FDD employs a Difference-Guided Dynamic Adapter to model bitemporal feature discrepancies and a Frequency-Decoupled Knowledge Distillation strategy to separate structural information from domain style for stable knowledge transfer.

In practice

• Apply difference-guided adaptation to focus on change-relevant features.
• Use frequency-decoupled distillation for knowledge transfer without historical data.
• Evaluate incremental learning models using F1 and IoU metrics across multiple domain sequences.

Topics

• Remote Sensing Change Detection
• Domain-Incremental Learning
• Catastrophic Forgetting
• Knowledge Distillation
• Frequency Domain
• Dynamic Adapters

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, Computer Vision Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computer Vision and Pattern Recognition.