遥感图像的语义分割对农业生产、城市规划等领域有十分重要的作用，但受成像距离、光照、地物、环境等因素影响，遥感图像中存在目标语义信息模糊问题，导致在分割时存在不确定性。针对此问题，提出一种多尺度上下文注意力方法（multi-scale context attention, MSCA），其将金字塔池化方法与注意力方法相结合，可以更充分地利用上下文信息。同时该方法显著降低了注意力方法的计算量和内存占用。在ISPRS Potsdam数据集上进行了实验，实验结果表明：MSCA方法在不显著增加内存开销，以及维持推理速度一致的情况下，对遥感图像中语义信息不明确的目标分类，具有更好的分割效果。

Semantic segmentation of remote sensing images plays a crucial role in agriculture production, urban planning, and other fields. However, due to factors like imaging distance, lighting conditions, objects, and environment, there is a problem of semantic ambiguity in remote sensing images, which leads to uncertainty in segmentation. A multi-scale context attention (MSCA) method that combined pyramid pooling with attention mechanisms to better utilize contextual information was proposed for this problem. Additionally, this method significantly reduced the computational complexity and memory usage of attention methods. Experimental results on the ISPRS Potsdam dataset demonstrate that the MSCA method achieves superior segmentation performance for target classification with ambiguous semantic information in remote sensing images while almost not increasing memory consumption and maintaining consistent inference speed.