A transformer-based siamese network for change detection
WGC Bandara, VM Patel - IGARSS 2022-2022 IEEE …, 2022 - ieeexplore.ieee.org
IGARSS 2022-2022 IEEE International Geoscience and Remote Sensing …, 2022•ieeexplore.ieee.org
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 …
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 …
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 and pre-trained models are available at github.com/wgcban/ChangeFormer.
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