# DASNet: Dual attentive fully convolutional siamese networks for change detection of high resolution satellite images

arXiv: https://arxiv.org/abs/2003.03608 · HTML: https://mlsanity.com/paper/2003.03608/

## Key facts

- Cited by 75 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Mar 2020.

## Built on by (most cited first, 30 of 75)

- [Remote Sensing Image Change Detection with Transformers](https://mlsanity.com/paper/2103.00208/) (2021)
- [Advancing Plain Vision Transformer Towards Remote Sensing Foundation Model](https://mlsanity.com/paper/2208.03987/) (2022)
- [An Empirical Study of Remote Sensing Pretraining](https://mlsanity.com/paper/2204.02825/) (2022)
- [RS-Mamba for Large Remote Sensing Image Dense Prediction](https://mlsanity.com/paper/2404.02668/) (2024)
- [S2Looking: A Satellite Side-Looking Dataset for Building Change Detection](https://mlsanity.com/paper/2107.09244/) (2021)
- [Transformers in Remote Sensing: A Survey](https://mlsanity.com/paper/2209.01206/) (2022)
- [Adapting Segment Anything Model for Change Detection in HR Remote Sensing Images](https://mlsanity.com/paper/2309.01429/) (2023)
- [Self-Supervised Material and Texture Representation Learning for Remote Sensing Tasks](https://mlsanity.com/paper/2112.01715/) (2021)
- [Joint Spatio-Temporal Modeling for the Semantic Change Detection in Remote Sensing Images](https://mlsanity.com/paper/2212.05245/) (2022)
- [Change Detection Methods for Remote Sensing in the Last Decade: A Comprehensive Review](https://mlsanity.com/paper/2305.05813/) (2023)
- [TINYCD: A (Not So) Deep Learning Model For Change Detection](https://mlsanity.com/paper/2207.13159/) (2022)
- [Global and Local Contrastive Self-Supervised Learning for Semantic Segmentation of HR Remote Sensing Images](https://mlsanity.com/paper/2106.10605/) (2021)
- [Revisiting Consistency Regularization for Semi-supervised Change Detection in Remote Sensing Images](https://mlsanity.com/paper/2204.08454/) (2022)
- [FCCDN: Feature Constraint Network for VHR Image Change Detection](https://mlsanity.com/paper/2105.10860/) (2021)
- [Super-resolution-based Change Detection Network with Stacked Attention Module for Images with Different Resolutions](https://mlsanity.com/paper/2103.00188/) (2021)
- [Remote Sensing Change Detection With Transformers Trained from Scratch](https://mlsanity.com/paper/2304.06710/) (2023)
- [BDANet: Multiscale Convolutional Neural Network with Cross-directional Attention for Building Damage Assessment from Satellite Images](https://mlsanity.com/paper/2105.07364/) (2021)
- [DDPM-CD: Denoising Diffusion Probabilistic Models as Feature Extractors for Change Detection](https://mlsanity.com/paper/2206.11892/) (2022)
- [ELGC-Net: Efficient Local-Global Context Aggregation for Remote Sensing Change Detection](https://mlsanity.com/paper/2403.17909/) (2024)
- [An End-to-end Supervised Domain Adaptation Framework for Cross-Domain Change Detection](https://mlsanity.com/paper/2204.00154/) (2022)
- [Semantic-CC: Boosting Remote Sensing Image Change Captioning via Foundational Knowledge and Semantic Guidance](https://mlsanity.com/paper/2407.14032/) (2024)
- [Semantic-aware Dense Representation Learning for Remote Sensing Image Change Detection](https://mlsanity.com/paper/2205.13769/) (2022)
- [Semantic-CD: Remote Sensing Image Semantic Change Detection towards Open-vocabulary Setting](https://mlsanity.com/paper/2501.06808/) (2025)
- [RDP-Net: Region Detail Preserving Network for Change Detection](https://mlsanity.com/paper/2202.09745/) (2022)
- [SARAS-Net: Scale and Relation Aware Siamese Network for Change Detection](https://mlsanity.com/paper/2212.01287/) (2022)
- [Adjacent-Level Feature Cross-Fusion With 3-D CNN for Remote Sensing Image Change Detection](https://mlsanity.com/paper/2302.05109/) (2023)
- [SAAN: Similarity-aware attention flow network for change detection with VHR remote sensing images](https://mlsanity.com/paper/2308.14570/) (2023)
- [ZeroSCD: Zero-Shot Street Scene Change Detection](https://mlsanity.com/paper/2409.15255/) (2024)
- [VcT: Visual change Transformer for Remote Sensing Image Change Detection](https://mlsanity.com/paper/2310.11417/) (2023)
- [A Survey of Sample-Efficient Deep Learning for Change Detection in Remote Sensing: Tasks, Strategies, and Challenges](https://mlsanity.com/paper/2502.02835/) (2025)

## Abstract (arXiv)

Change detection is a basic task of remote sensing image processing. The research objective is to identity the change information of interest and filter out the irrelevant change information as interference factors. Recently, the rise of deep learning has provided new tools for change detection, which have yielded impressive results. However, the available methods focus mainly on the difference information between multitemporal remote sensing images and lack robustness to pseudo-change information. To overcome the lack of resistance of current methods to pseudo-changes, in this paper, we propose a new method, namely, dual attentive fully convolutional Siamese networks (DASNet) for change detection in high-resolution images. Through the dual-attention mechanism, long-range dependencies are captured to obtain more discriminant feature representations to enhance the recognition performance of the model. Moreover, the imbalanced sample is a serious problem in change detection, i.e. unchanged samples are much more than changed samples, which is one of the main reasons resulting in pseudo-changes. We put forward the weighted double margin contrastive loss to address this problem by punishing the attention to unchanged feature pairs and increase attention to changed feature pairs. The experimental results of our method on the change detection dataset (CDD) and the building change detection dataset (BCDD) demonstrate that compared with other baseline methods, the proposed method realizes maximum improvements of 2.1% and 3.6%, respectively, in the F1 score. Our Pytorch implementation is available at https://github.com/lehaifeng/DASNet.