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

arXiv: https://arxiv.org/abs/2601.16573 · HTML: https://mlsanity.com/paper/2601.16573/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 42 references that are in the map.
- First posted on arXiv 23 Jan 2026.

## Builds on (most cited first, 18 of 18)

- [InternImage: Exploring Large-Scale Vision Foundation Models with Deformable Convolutions](https://mlsanity.com/paper/2211.05778/) (2022)
- [Fully Convolutional Siamese Networks for Change Detection](https://mlsanity.com/paper/1810.08462/) (2018)
- [Remote Sensing Image Change Detection with Transformers](https://mlsanity.com/paper/2103.00208/) (2021)
- [A Transformer-Based Siamese Network for Change Detection](https://mlsanity.com/paper/2201.01293/) (2022)
- [ChangeMamba: Remote Sensing Change Detection With Spatiotemporal State Space Model](https://mlsanity.com/paper/2404.03425/) (2024)
- [Urban Change Detection for Multispectral Earth Observation Using Convolutional Neural Networks](https://mlsanity.com/paper/1810.08468/) (2018)
- [RSMamba: Remote Sensing Image Classification with State Space Model](https://mlsanity.com/paper/2403.19654/) (2024)
- [DASNet: Dual attentive fully convolutional siamese networks for change detection of high resolution satellite images](https://mlsanity.com/paper/2003.03608/) (2020)
- [EMCAD: Efficient Multi-scale Convolutional Attention Decoding for Medical Image Segmentation](https://mlsanity.com/paper/2405.06880/) (2024)
- [CDMamba: Incorporating Local Clues into Mamba for Remote Sensing Image Binary Change Detection](https://mlsanity.com/paper/2406.04207/) (2024)
- [Bi-Temporal Semantic Reasoning for the Semantic Change Detection in HR Remote Sensing Images](https://mlsanity.com/paper/2108.06103/) (2021)
- [Remote Sensing Change Detection With Transformers Trained from Scratch](https://mlsanity.com/paper/2304.06710/) (2023)
- [CM-UNet: Hybrid CNN-Mamba UNet for Remote Sensing Image Semantic Segmentation](https://mlsanity.com/paper/2405.10530/) (2024)
- [ChangeViT: Unleashing Plain Vision Transformers for Change Detection](https://mlsanity.com/paper/2406.12847/) (2024)
- [MSA2Net: Multi-scale Adaptive Attention-guided Network for Medical Image Segmentation](https://mlsanity.com/paper/2407.21640/) (2024)
- [SignEye: Traffic Sign Interpretation from Vehicle First-Person View](https://mlsanity.com/paper/2411.11507/) (2024)
- [Real-Time Text Detection with Similar Mask in Traffic, Industrial, and Natural Scenes](https://mlsanity.com/paper/2411.02794/) (2024)
- [Edge Approximation Text Detector](https://mlsanity.com/paper/2504.04001/) (2025)

## Abstract (arXiv)

Remote sensing change detection (RSCD) aims to identify the spatio-temporal changes of land cover, providing critical support for multi-disciplinary 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 exhibiting sensitivity to radiometric and geometric noise. Following the above issues, we propose a dual-module collaboration guided hierarchical adaptive aggregation framework, namely HA2F, which consists of dynamic hierarchical feature calibration module (DHFCM) and noise-adaptive feature refinement module (NAFRM). The former dynamically fuses adjacent-level features through perceptual feature selection, suppressing irrelevant discrepancies to address multi-temporal 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. hrefhttps://huggingface.co/InPeerReview/RemoteSensingChangeDetection-RSCD.HA2FHA2F Official Code is Available Here!