A Transformer-Based Siamese Network for Change Detection

Code

• github.com/wgcban/changeformer
  OfficialIn paperpytorch★ 585
• github.com/likyoo/open-cd
  pytorch★ 831
• github.com/PaddlePaddle/PaddleRS
  paddle★ 479

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.

Tasks

Benchmark Results

Reproductions