Semantic segmentation in high resolution aerial images is a challenging task in remote sensing fields. Compared with other scenarios, semantic segmentation of remote sensing images requires larger receptive fields and more global information. The attention mechanism is one of the most effective way to integrate local features. In this paper, a Triple Attention Network (TANet) is proposed to get more global features. In specific, the paper introduce two self-attention module to get position attention and channel attention. And a label attention module, which generated the attention probability map by introducing spatial context information in the label. The experimental results shows that the proposed network has a higher FWIoU and PA scores than other networks.