Spatial attention mechanism has been widely used in semantic segmentation of
remote sensing images given its capability to model long-range dependencies.
Many methods adopting spatial attention mechanism aggregate contextual
information using direct relationships between pixels within an image, while
ignoring the scene awareness of pixels (i.e., being aware of the global context
of the scene where the pixels are located and perceiving their relative
positions). Given the observation that scene awareness benefits context
modeling with spatial correlations of ground objects, we design a scene-aware
attention module based on a refined spatial attention mechanism embedding scene
awareness. Besides, we present a local-global class attention mechanism to
address the problem that general attention mechanism introduces excessive
background noises while hardly considering the large intra-class variance in
remote sensing images. In this paper, we integrate both scene-aware and class
attentions to propose a scene-aware class attention network (SACANet) for
semantic segmentation of remote sensing images. Experimental results on three
datasets show that SACANet outperforms other state-of-the-art methods and
validate its effectiveness. Code is available at
this https URL

本文提出了一种基于场景感知注意力的空间注意力模块和局部 - 全局类注意力机制，以此构建一种应用于遥感图像语义分割的场景感知类别注意力网络（SACANet），试验表明 SACANet 的性能优于其他最先进的方法并验证了其有效性。