# Exchanging Dual Encoder-Decoder: A New Strategy for Change Detection with Semantic Guidance and Spatial Localization

arXiv: https://arxiv.org/abs/2311.11302 · HTML: https://mlsanity.com/paper/2311.11302/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 63 references that are in the map.
- First posted on arXiv 19 Nov 2023.

## Builds on (most cited first, 17 of 17)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Pyramid Scene Parsing Network](https://mlsanity.com/paper/1612.01105/) (2016)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting](https://mlsanity.com/paper/1506.04214/) (2015)
- [Attention U-Net: Learning Where to Look for the Pancreas](https://mlsanity.com/paper/1804.03999/) (2018)
- [ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design](https://mlsanity.com/paper/1807.11164/) (2018)
- [ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks](https://mlsanity.com/paper/1910.03151/) (2019)
- [Albumentations: fast and flexible image augmentations](https://mlsanity.com/paper/1809.06839/) (2018)
- [Using Pre-Training Can Improve Model Robustness and Uncertainty](https://mlsanity.com/paper/1901.09960/) (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)
- [Building Change Detection for Remote Sensing Images Using a Dual Task Constrained Deep Siamese Convolutional Network Model](https://mlsanity.com/paper/1909.07726/) (2019)
- [FCCDN: Feature Constraint Network for VHR Image Change Detection](https://mlsanity.com/paper/2105.10860/) (2021)
- [Detecting Urban Changes with Recurrent Neural Networks from Multitemporal Sentinel-2 Data](https://mlsanity.com/paper/1910.07778/) (2019)
- [Looking for change? Roll the Dice and demand Attention](https://mlsanity.com/paper/2009.02062/) (2020)

## Built on by (most cited first, 21 of 21)

- [ChangeMamba: Remote Sensing Change Detection With Spatiotemporal State Space Model](https://mlsanity.com/paper/2404.03425/) (2024)
- [RS-Mamba for Large Remote Sensing Image Dense Prediction](https://mlsanity.com/paper/2404.02668/) (2024)
- [MTP: Advancing Remote Sensing Foundation Model via Multi-Task Pretraining](https://mlsanity.com/paper/2403.13430/) (2024)
- [LSKNet: A Foundation Lightweight Backbone for Remote Sensing](https://mlsanity.com/paper/2403.11735/) (2024)
- [Visual Instruction Pretraining for Domain-Specific Foundation Models](https://mlsanity.com/paper/2509.17562/) (2025)
- [PeftCD: Leveraging Vision Foundation Models with Parameter-Efficient Fine-Tuning for Remote Sensing Change Detection](https://mlsanity.com/paper/2509.09572/) (2025)
- [EfficientCD: A New Strategy For Change Detection Based With Bi-temporal Layers Exchanged](https://mlsanity.com/paper/2407.15999/) (2024)
- [FDG-Diff: Frequency-Domain-Guided Diffusion Framework for Compressed Hazy Image Restoration](https://mlsanity.com/paper/2501.12832/) (2025)
- [Exploring Foundation Models in Remote Sensing Image Change Detection: A Comprehensive Survey](https://mlsanity.com/paper/2410.07824/) (2024)
- [3D-SSM: A Novel 3D Selective Scan Module for Remote Sensing Change Detection](https://mlsanity.com/paper/2506.19263/) (2025)
- [Mamba-FCS: Joint Spatio- Frequency Feature Fusion, Change-Guided Attention, and SeK Loss for Enhanced Semantic Change Detection in Remote Sensing](https://mlsanity.com/paper/2508.08232/) (2025)
- [SChanger: Change Detection from a Semantic Change and Spatial Consistency Perspective](https://mlsanity.com/paper/2503.20734/) (2025)
- [CGEarthEye:A High-Resolution Remote Sensing Vision Foundation Model Based on the Jilin-1 Satellite Constellation](https://mlsanity.com/paper/2507.00356/) (2025)
- [Wavelet-Guided Dual-Frequency Encoding for Remote Sensing Change Detection](https://mlsanity.com/paper/2508.05271/) (2025)
- [RPD-Diff: Region-Adaptive Physics-Guided Diffusion Model for Visibility Enhancement under Dense and Non-Uniform Haze](https://mlsanity.com/paper/2508.16956/) (2025)
- [UDHF2-Net: Uncertainty-diffusion-model-based High-Frequency TransFormer Network for Remotely Sensed Imagery Interpretation](https://mlsanity.com/paper/2406.16129/) (2024)
- [A Remote Sensing Image Change Detection Method Integrating Layer Exchange and Channel-Spatial Differences](https://mlsanity.com/paper/2501.10905/) (2025)
- [Spatial-Temporal-Spectral Unified Modeling for Remote Sensing Dense Prediction](https://mlsanity.com/paper/2505.12280/) (2025)
- [CEBSNet: Change-Excited and Background-Suppressed Network with Temporal Dependency Modeling for Bitemporal Change Detection](https://mlsanity.com/paper/2505.15322/) (2025)
- [AtrousMamaba: An Atrous-Window Scanning Visual State Space Model for Remote Sensing Change Detection](https://mlsanity.com/paper/2507.16172/) (2025)
- [Exchange Is All You Need for Remote Sensing Change Detection](https://mlsanity.com/paper/2601.07805/) (2026)

## Abstract (arXiv)

Change detection is a critical task in earth observation applications. Recently, deep learning-based methods have shown promising performance and are quickly adopted in change detection. However, the widely used multiple encoder and single decoder (MESD) as well as dual encoder-decoder (DED) architectures still struggle to effectively handle change detection well. The former has problems of bitemporal feature interference in the feature-level fusion, while the latter is inapplicable to intraclass change detection and multiview building change detection. To solve these problems, we propose a new strategy with an exchanging dual encoder-decoder structure for binary change detection with semantic guidance and spatial localization. The proposed strategy solves the problems of bitemporal feature inference in MESD by fusing bitemporal features in the decision level and the inapplicability in DED by determining changed areas using bitemporal semantic features. We build a binary change detection model based on this strategy, and then validate and compare it with 18 state-of-the-art change detection methods on six datasets in three scenarios, including intraclass change detection datasets (CDD, SYSU), single-view building change detection datasets (WHU, LEVIR-CD, LEVIR-CD+) and a multiview building change detection dataset (NJDS). The experimental results demonstrate that our model achieves superior performance with high efficiency and outperforms all benchmark methods with F1-scores of 97.77%, 83.07%, 94.86%, 92.33%, 91.39%, 74.35% on CDD, SYSU, WHU, LEVIR-CD, LEVIR- CD+, and NJDS datasets, respectively. The code of this work will be available at https://github.com/NJU-LHRS/official-SGSLN.