# Super-resolution-based Change Detection Network with Stacked Attention Module for Images with Different Resolutions

arXiv: https://arxiv.org/abs/2103.00188 · HTML: https://mlsanity.com/paper/2103.00188/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 50 references that are in the map.
- First posted on arXiv 27 Feb 2021.

## Builds on (most cited first, 15 of 15)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network](https://mlsanity.com/paper/1609.04802/) (2016)
- [Image Super-Resolution Using Deep Convolutional Networks](https://mlsanity.com/paper/1501.00092/) (2014)
- [Accurate Image Super-Resolution Using Very Deep Convolutional Networks](https://mlsanity.com/paper/1511.04587/) (2015)
- [Deeply-Recursive Convolutional Network for Image Super-Resolution](https://mlsanity.com/paper/1511.04491/) (2015)
- [Fully Convolutional Siamese Networks for Change Detection](https://mlsanity.com/paper/1810.08462/) (2018)
- [Recurrent Neural Network Regularization](https://mlsanity.com/paper/1409.2329/) (2014)
- [Urban Change Detection for Multispectral Earth Observation Using Convolutional Neural Networks](https://mlsanity.com/paper/1810.08468/) (2018)
- [DASNet: Dual attentive fully convolutional siamese networks for change detection of high resolution satellite images](https://mlsanity.com/paper/2003.03608/) (2020)
- [Detecting Urban Changes with Recurrent Neural Networks from Multitemporal Sentinel-2 Data](https://mlsanity.com/paper/1910.07778/) (2019)

## Built on by (most cited first, 23 of 23)

- [An Empirical Study of Remote Sensing Pretraining](https://mlsanity.com/paper/2204.02825/) (2022)
- [MTP: Advancing Remote Sensing Foundation Model via Multi-Task Pretraining](https://mlsanity.com/paper/2403.13430/) (2024)
- [CDMamba: Incorporating Local Clues into Mamba for Remote Sensing Image Binary Change Detection](https://mlsanity.com/paper/2406.04207/) (2024)
- [HANet: A Hierarchical Attention Network for Change Detection With Bitemporal Very-High-Resolution Remote Sensing Images](https://mlsanity.com/paper/2404.09178/) (2024)
- [Remote Sensing Change Detection With Transformers Trained from Scratch](https://mlsanity.com/paper/2304.06710/) (2023)
- [Building Bridges across Spatial and Temporal Resolutions: Reference-Based Super-Resolution via Change Priors and Conditional Diffusion Model](https://mlsanity.com/paper/2403.17460/) (2024)
- [Fully Transformer Network for Change Detection of Remote Sensing Images](https://mlsanity.com/paper/2210.00757/) (2022)
- [TransY-Net:Learning Fully Transformer Networks for Change Detection of Remote Sensing Images](https://mlsanity.com/paper/2310.14214/) (2023)
- [Inferring 3D change detection from bitemporal optical images](https://mlsanity.com/paper/2205.15903/) (2022)
- [Visual Instruction Pretraining for Domain-Specific Foundation Models](https://mlsanity.com/paper/2509.17562/) (2025)
- [Continuous Cross-resolution Remote Sensing Image Change Detection](https://mlsanity.com/paper/2305.14722/) (2023)
- [FoBa: A Foreground-Background co-Guided Method and New Benchmark for Remote Sensing Semantic Change Detection](https://mlsanity.com/paper/2509.15788/) (2025)
- [HiCD: Change Detection in Quality-Varied Images via Hierarchical Correlation Distillation](https://mlsanity.com/paper/2401.10752/) (2024)
- [SceneDiff: A Benchmark and Method for Multiview Object Change Detection](https://mlsanity.com/paper/2512.16908/) (2025)
- [BCE-Net: Reliable Building Footprints Change Extraction based on Historical Map and Up-to-Date Images using Contrastive Learning](https://mlsanity.com/paper/2304.07076/) (2023)
- [Adaptive Semantic-Enhanced Denoising Diffusion Probabilistic Model for Remote Sensing Image Super-Resolution](https://mlsanity.com/paper/2403.11078/) (2024)
- [DeepCL: Deep Change Feature Learning on Remote Sensing Images in the Metric Space](https://mlsanity.com/paper/2307.12208/) (2023)
- [BD-MSA: Body decouple VHR Remote Sensing Image Change Detection method guided by multi-scale feature information aggregation](https://mlsanity.com/paper/2401.04330/) (2024)
- [AGSPNet: A framework for parcel-scale crop fine-grained semantic change detection from UAV high-resolution imagery with agricultural geographic scene constraints](https://mlsanity.com/paper/2401.06252/) (2024)
- [HSONet:A Siamese foreground association-driven hard case sample optimization network for high-resolution remote sensing image change detection](https://mlsanity.com/paper/2402.16242/) (2024)
- [Do Satellite Tasks Need Special Pretraining?](https://mlsanity.com/paper/2510.17014/) (2025)
- [Magnifying change: Rapid burn scar mapping with multi-resolution, multi-source satellite imagery](https://mlsanity.com/paper/2601.09262/) (2026)
- [FM-ChangeNet: Learning Change through Pathwise Feature Transport](https://mlsanity.com/paper/2607.04750/) (2026)

## Abstract (arXiv)

Change detection, which aims to distinguish surface changes based on bi-temporal images, plays a vital role in ecological protection and urban planning. Since high resolution (HR) images cannot be typically acquired continuously over time, bi-temporal images with different resolutions are often adopted for change detection in practical applications. Traditional subpixel-based methods for change detection using images with different resolutions may lead to substantial error accumulation when HR images are employed; this is because of intraclass heterogeneity and interclass similarity. Therefore, it is necessary to develop a novel method for change detection using images with different resolutions, that is more suitable for HR images. To this end, we propose a super-resolution-based change detection network (SRCDNet) with a stacked attention module. The SRCDNet employs a super resolution (SR) module containing a generator and a discriminator to directly learn SR images through adversarial learning and overcome the resolution difference between bi-temporal images. To enhance the useful information in multi-scale features, a stacked attention module consisting of five convolutional block attention modules (CBAMs) is integrated to the feature extractor. The final change map is obtained through a metric learning-based change decision module, wherein a distance map between bi-temporal features is calculated. The experimental results demonstrate the superiority of the proposed method, which not only outperforms all baselines -with the highest F1 scores of 87.40% on the building change detection dataset and 92.94% on the change detection dataset -but also obtains the best accuracies on experiments performed with images having a 4x and 8x resolution difference. The source code of SRCDNet will be available at https://github.com/liumency/SRCDNet.