A Transformer-Based Siamese Network for Change Detection
- URL: http://arxiv.org/abs/2201.01293v1
- Date: Tue, 4 Jan 2022 18:55:22 GMT
- Title: A Transformer-Based Siamese Network for Change Detection
- Authors: Wele Gedara Chaminda Bandara and Vishal M. Patel
- Abstract summary: This paper presents a transformer-based Siamese network architecture (abbreviated by ChangeFormer) for Change Detection (CD)
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.
- Score: 72.04912755926524
- License: http://creativecommons.org/licenses/by/4.0/
- 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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