# HANet: A Hierarchical Attention Network for Change Detection With Bitemporal Very-High-Resolution Remote Sensing Images

arXiv: https://arxiv.org/abs/2404.09178 · HTML: https://mlsanity.com/paper/2404.09178/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 55 references that are in the map.
- First posted on arXiv 14 Apr 2024.

## Builds on (most cited first, 21 of 21)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [UNet++: A Nested U-Net Architecture for Medical Image Segmentation](https://mlsanity.com/paper/1807.10165/) (2018)
- [Show, Attend and Tell: Neural Image Caption Generation with Visual Attention](https://mlsanity.com/paper/1502.03044/) (2015)
- [Dual Attention Network for Scene Segmentation](https://mlsanity.com/paper/1809.02983/) (2018)
- [Attention-Based Models for Speech Recognition](https://mlsanity.com/paper/1506.07503/) (2015)
- [Axial Attention in Multidimensional Transformers](https://mlsanity.com/paper/1912.12180/) (2019)
- [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)
- [ViTAE: Vision Transformer Advanced by Exploring Intrinsic Inductive Bias](https://mlsanity.com/paper/2106.03348/) (2021)
- [An Empirical Study of Remote Sensing Pretraining](https://mlsanity.com/paper/2204.02825/) (2022)
- [Learning RoI Transformer for Detecting Oriented Objects in Aerial Images](https://mlsanity.com/paper/1812.00155/) (2018)
- [Deep Roots: Improving CNN Efficiency with Hierarchical Filter Groups](https://mlsanity.com/paper/1605.06489/) (2016)
- [Super-resolution-based Change Detection Network with Stacked Attention Module for Images with Different Resolutions](https://mlsanity.com/paper/2103.00188/) (2021)
- [Detecting Urban Changes with Recurrent Neural Networks from Multitemporal Sentinel-2 Data](https://mlsanity.com/paper/1910.07778/) (2019)
- [Semantic-aware Dense Representation Learning for Remote Sensing Image Change Detection](https://mlsanity.com/paper/2205.13769/) (2022)
- [Consistency-Regularized Region-Growing Network for Semantic Segmentation of Urban Scenes with Point-Level Annotations](https://mlsanity.com/paper/2202.03740/) (2022)
- [HyperNet: Self-Supervised Hyperspectral Spatial-Spectral Feature Understanding Network for Hyperspectral Change Detection](https://mlsanity.com/paper/2207.09634/) (2022)

## Built on by (most cited first, 30 of 30)

- [HyperSIGMA: Hyperspectral Intelligence Comprehension Foundation Model](https://mlsanity.com/paper/2406.11519/) (2024)
- [Open-CD: A Comprehensive Toolbox for Change Detection](https://mlsanity.com/paper/2407.15317/) (2024)
- [MaskCD: A Remote Sensing Change Detection Network Based on Mask Classification](https://mlsanity.com/paper/2404.12081/) (2024)
- [C2F-SemiCD: A Coarse-to-Fine Semi-Supervised Change Detection Method Based on Consistency Regularization in High-Resolution Remote Sensing Images](https://mlsanity.com/paper/2404.13838/) (2024)
- [UniVCD: A New Method for Unsupervised Change Detection in the Open-Vocabulary Era](https://mlsanity.com/paper/2512.13089/) (2025)
- [JL1-CD: A New Benchmark for Remote Sensing Change Detection and a Robust Multi-Teacher Knowledge Distillation Framework](https://mlsanity.com/paper/2502.13407/) (2025)
- [M2CD: A Unified MultiModal Framework for Optical-SAR Change Detection with Mixture of Experts and Self-Distillation](https://mlsanity.com/paper/2503.19406/) (2025)
- [AI for Scientific Discovery is a Social Problem](https://mlsanity.com/paper/2509.06580/) (2025)
- [ViLaCD-R1: A Vision-Language Framework for Semantic Change Detection in Remote Sensing](https://mlsanity.com/paper/2512.23244/) (2025)
- [Efficient Fourier Filtering Network with Contrastive Learning for AAV-based Unaligned Bimodal Salient Object Detection](https://mlsanity.com/paper/2411.03728/) (2024)
- [SChanger: Change Detection from a Semantic Change and Spatial Consistency Perspective](https://mlsanity.com/paper/2503.20734/) (2025)
- [Efficient Remote Sensing Change Detection with Change State Space Models](https://mlsanity.com/paper/2504.11080/) (2025)
- [CGEarthEye:A High-Resolution Remote Sensing Vision Foundation Model Based on the Jilin-1 Satellite Constellation](https://mlsanity.com/paper/2507.00356/) (2025)
- [Compress-Align-Detect: onboard change detection from unregistered images](https://mlsanity.com/paper/2507.15578/) (2025)
- [Revisiting Change Detection Methods for their Application to Serac Fall Time-Lapse Monitoring](https://mlsanity.com/paper/2605.28100/) (2026)
- [Overlapping Schwarz Attention: Hierarchical Attention via Domain Decomposition](https://mlsanity.com/paper/2606.18525/) (2026)
- [Bi-temporal Gaussian Feature Dependency Guided Change Detection in Remote Sensing Images](https://mlsanity.com/paper/2410.09539/) (2024)
- [HSANET: A Hybrid Self-Cross Attention Network For Remote Sensing Change Detection](https://mlsanity.com/paper/2504.15170/) (2025)
- [Spatial-Temporal-Spectral Unified Modeling for Remote Sensing Dense Prediction](https://mlsanity.com/paper/2505.12280/) (2025)
- [RS-TinyNet: Stage-wise Feature Fusion Network for Detecting Tiny Objects in Remote Sensing Images](https://mlsanity.com/paper/2507.13120/) (2025)
- [Revisiting Adversarial Training under Hyperspectral Image](https://mlsanity.com/paper/2510.01014/) (2025)
- [A Spatial Semantics and Continuity Perception Attention for Remote Sensing Water Body Change Detection](https://mlsanity.com/paper/2511.16143/) (2025)
- [Exchange Is All You Need for Remote Sensing Change Detection](https://mlsanity.com/paper/2601.07805/) (2026)
- [PhyUnfold-Net: Advancing Remote Sensing Change Detection with Physics-Guided Deep Unfolding](https://mlsanity.com/paper/2603.19566/) (2026)
- [Content-Induced Spatial-Spectral Aggregation Network for Change Detection in Remote Sensing Images](https://mlsanity.com/paper/2606.10328/) (2026)
- [Building Change Detection in Earthquake: A Multi-Scale Interaction Network and A Change Detection Dataset](https://mlsanity.com/paper/2606.10329/) (2026)
- [UrbanCDNet: Appearance-Robust and Boundary-Aware Bitemporal Change Detection for Korean Urban Building Monitoring](https://mlsanity.com/paper/2606.29781/) (2026)
- [JL1-CC&QA: Extending the JL1-CD Benchmark with Change Captioning and Question Answering](https://mlsanity.com/paper/2606.31745/) (2026)
- [Dual-Selective Network for Domain-Incremental Change Detection](https://mlsanity.com/paper/2607.02299/) (2026)
- [iVISION-2DCD: A Long-Term Change Detection Dataset for Large-Scale Outdoor Construction Monitoring](https://mlsanity.com/paper/2607.03553/) (2026)

## Abstract (arXiv)

Benefiting from the developments in deep learning technology, deep-learning-based algorithms employing automatic feature extraction have achieved remarkable performance on the change detection (CD) task. However, the performance of existing deep-learning-based CD methods is hindered by the imbalance between changed and unchanged pixels. To tackle this problem, a progressive foreground-balanced sampling strategy on the basis of not adding change information is proposed in this article to help the model accurately learn the features of the changed pixels during the early training process and thereby improve detection performance.Furthermore, we design a discriminative Siamese network, hierarchical attention network (HANet), which can integrate multiscale features and refine detailed features. The main part of HANet is the HAN module, which is a lightweight and effective self-attention mechanism. Extensive experiments and ablation studies on two CDdatasets with extremely unbalanced labels validate the effectiveness and efficiency of the proposed method.