Convolution and Attention Mixer for Synthetic Aperture Radar Image
  Change Detection
Haopeng Zhang Zijing Lin Feng Gao Junyu Dong Qian Du Heng-Chao Li
Synthetic aperture radar (SAR) image change detection is a critical task and
has received increasing attentions in the remote sensing community. However,
existing SAR change detection methods are mainly based on convolutional neural
networks (CNNs), with limited consideration of global attention mechanism. In
this letter, we explore Transformer-like architecture for SAR change detection
to incorporate global attention. To this end, we propose a convolution and
attention mixer (CAMixer). First, to compensate the inductive bias for
Transformer, we combine self-attention with shift convolution in a parallel
way. The parallel design effectively captures the global semantic information
via the self-attention and performs local feature extraction through shift
convolution simultaneously. Second, we adopt a gating mechanism in the
feed-forward network to enhance the non-linear feature transformation. The
gating mechanism is formulated as the element-wise multiplication of two
parallel linear layers. Important features can be highlighted, leading to
high-quality representations against speckle noise. Extensive experiments
conducted on three SAR datasets verify the superior performance of the proposed
CAMixer. The source codes will be publicly available at
https://github.com/summitgao/CAMixer .