HA2F: Dual-Module Collaboration-Guided Hierarchical Adaptive Aggregation Framework for Remote Sensing Change Detection

2026-01-23

IEEE Transactions on Geoscience and Remote Sensing

AI Summary

This work addresses the challenges of misaligned multi-temporal features and high sensitivity to radiometric and geometric noise in remote sensing change detection by proposing a dual-module co-guided hierarchical adaptive aggregation framework. The method employs a dynamic hierarchical feature calibration module to align cross-layer semantics and integrates a noise-adaptive feature refinement module to suppress irrelevant discrepancies and distracting regions. Furthermore, a perceptual feature selection mechanism generates dual-path spatial masks to effectively highlight changed areas. Evaluated on the LEVIR-CD, WHU-CD, and SYSU-CD datasets, the proposed approach consistently outperforms existing methods in both detection accuracy and computational efficiency, significantly enhancing the robustness and localization capability of change detection.

Technology Category

• Computer Vision: Remote Sensing / Geospatial AI
• Machine Learning: Multi-instance/Multi-view Learning
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

Remote sensing change detection (RSCD) aims to identify the spatio-temporal changes of land cover, providing critical support for multidisciplinary applications (e.g., environmental monitoring, disaster assessment, and climate change studies). Existing methods focus either on extracting features from localized patches or pursue processing entire images holistically, which leads to the cross-temporal feature matching deviation and exhibits sensitivity to radiometric and geometric noise. Following the above issues, we propose a dual-module collaboration-guided hierarchical adaptive aggregation framework (HA2F), namely HA2F, which consists of a dynamic hierarchical feature calibration module (DHFCM) and a noise-adaptive feature refinement module (NAFRM). The former dynamically fuses adjacent-level features through perceptual feature selection, suppressing irrelevant discrepancies to address multitemporal feature alignment deviations. The NAFRM utilizes the dual feature selection mechanism to highlight the change-sensitive regions and generate spatial masks, suppressing the interference of irrelevant regions or shadows. Extensive experiments verify the effectiveness of the proposed HA2F, which achieves state-of-the-art performance on LEVIR-CD, WHU-CD, and SYSU-CD datasets, surpassing existing comparative methods in terms of both precision metrics and computational efficiency. In addition, ablation experiments show that DHFCM and NAFRM are effective. Code can be found at https://huggingface.co/InPeerReview/RemoteSensingChangeDetection-RSCD.HA2F.

Problem

• Remote sensing change detection
• feature matching deviation
• radiometric noise
• geometric noise
• multi-temporal alignment

Innovation

• Hierarchical Adaptive Aggregation
• Dynamic Feature Calibration
• Noise-Adaptive Refinement
• Dual-Module Collaboration
• Remote Sensing Change Detection