EoCD: Encoder only Remote Sensing Change Detection
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Abstract (arXiv)
Being a cornerstone of temporal analysis, change detection has been playing a pivotal role in modern earth observation. Existing change detection methods rely on the Siamese encoder to individually extract temporal features followed by temporal fusion. Subsequently, these methods design sophisticated decoders to improve the change detection performance without taking into consideration the complexity of the model. These aforementioned issues intensify the overall computational cost as well as the network's complexity which is undesirable. Alternatively, few methods utilize the early fusion scheme to combine the temporal images. These methods prevent the extra overhead of Siamese encoder, however, they also rely on sophisticated decoders for better performance. In addition, these methods demonstrate inferior performance as compared to late fusion based methods. To bridge these gaps, we introduce encoder only change detection (EoCD) that is a simple and effective method for the change detection task. The proposed method performs the early fusion of the temporal data and replaces the decoder with a parameter-free multiscale feature fusion module thereby significantly reducing the overall complexity of the model. EoCD demonstrate the optimal balance between the change detection performance and the prediction speed across a variety of encoder architectures. Additionally, EoCD demonstrate that the performance of the model is predominantly dependent on the encoder network, making the decoder an additional component. Extensive experimentation on four challenging change detection datasets reveals the effectiveness of the proposed method.
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References · 25
- 2015Deep Residual Learning for Image Recognitioncited by 51,809
- 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
- 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,082
- 2023Mamba: Linear-Time Sequence Modeling with Selective State Spacescited by 4,616
- 2021SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformerscited by 2,923
- 2024Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Modelcited by 1,051
- 2018Fully Convolutional Siamese Networks for Change Detectioncited by 305
- 2021Remote Sensing Image Change Detection with Transformerscited by 305
- 2022A Transformer-Based Siamese Network for Change Detectioncited by 243
- 2022Focal Modulation Networkscited by 186
- 2024PlainMamba: Improving Non-Hierarchical Mamba in Visual Recognitioncited by 141
- 2024ChangeMamba: Remote Sensing Change Detection With Spatiotemporal State Space Modelcited by 118
13 more
- 2019Building Change Detection for Remote Sensing Images Using a Dual Task Constrained Deep Siamese Convolutional Network Modelcited by 84
- 2024RS-Mamba for Large Remote Sensing Image Dense Predictioncited by 80
- 2020DASNet: Dual attentive fully convolutional siamese networks for change detection of high resolution satellite imagescited by 75
- 2025Your ViT is Secretly an Image Segmentation Modelcited by 61
- 2024Vivim: a Video Vision Mamba for Medical Video Segmentationcited by 51
- 2024CDMamba: Incorporating Local Clues into Mamba for Remote Sensing Image Binary Change Detectioncited by 48
- 2023Remote Sensing Change Detection With Transformers Trained from Scratchcited by 22
- 2024ELGC-Net: Efficient Local-Global Context Aggregation for Remote Sensing Change Detectioncited by 19
- 2021WRICNet:A Weighted Rich-scale Inception Coder Network for Multi-Resolution Remote Sensing Image Change Detectioncited by 5
- 2024LCD-Net: A Lightweight Remote Sensing Change Detection Network Combining Feature Fusion and Gating Mechanismcited by 5
- 2024DeBiFormer: Vision Transformer with Deformable Agent Bi-level Routing Attentioncited by 4
- 2025FSG-Net: Frequency-Spatial Synergistic Gated Network for High-Resolution Remote Sensing Change Detectioncited by 2
- 2025HyRet-Change: A hybrid retentive network for remote sensing change detectioncited by 1
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