A Transformer-Based Siamese Network for Change Detection

Wele Gedara Chaminda Bandara, Vishal M. Patel
1/4/2022
cs.CV

Official code: wgcban/ChangeFormer (627 stars, MIT, PyTorch, last commit 2024-01-31).

Abstract

This paper presents a transformer-based Siamese network architecture (abbreviated by ChangeFormer) for Change Detection (CD) from a pair of co-registered remote sensing images. Different from recent CD frameworks, which are based on fully convolutional networks (ConvNets), the proposed method unifies hierarchically structured transformer encoder with Multi-Layer Perception (MLP) decoder in a Siamese network architecture to efficiently render multi-scale long-range details required for accurate CD. Experiments on two CD datasets show that the proposed end-to-end trainable ChangeFormer architecture achieves better CD performance than previous counterparts. Our code is available at https://github.com/wgcban/ChangeFormer.

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Code Implementations(1)

wgcban/ChangeFormerOfficial100%
PyTorchMIT

[IGARSS'22]: A Transformer-Based Siamese Network for Change Detection

62786Shell, MATLABDec 2, 20212 years agoMIT
attention-mechanismchange-detectionclimate-changedeep-learningmulti-temporal+6 more

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Cite this paper

@article{bandara2022transformerbased,
  title  = {A Transformer-Based Siamese Network for Change Detection},
  author = {Wele Gedara Chaminda Bandara and Vishal M. Patel},
  year   = {2022},
  eprint = {2201.01293},
  archivePrefix = {arXiv},
  url    = {https://arxiv.org/abs/2201.01293}
}

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