Global Context Parallel Attention for Anchor-Free Instance Segmentation in Remote Sensing Images

Segmenting objects in optical remote sensing images has always been a hot topic for remote sensing image researchers. However, many previous works used segmentation algorithms designed for common objects without modification, leading to slow and poor results. In this work, we exploit self-attention mechanism into anchor-free segmentation architectures to improve the segmentation accuracy for objects in high-resolution remote sensing images. The proposed module integrates the self-attention mechanism, namely the global context parallel attention module (GC-PAM). It is composed of a parallel global context channel self-attention block and a spatial self-attention block. By implementing our GC-PAM in an anchor-free network, the channel-wise and spatial-wise weights are both reassigned, which can improve the segmentation accuracy significantly.