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Deep convolutional neural networks have made significant progress in the field of intelligent analysis of remote-sensing images. However, the semantic segmentation task in high-resolution remote-sensing (HRRS) images always faces the problem of large-scale variation and complex background samples, which causes difficulties in distinguishing confusable ground objects. In this letter, we propose a novel multipath feature refinement network (MFRNet) to alleviate the above problems. We design the feature refinement module (FRM) to fuse features at various scales, which helps to capture different levels of spatial information. It also alleviates the boundary ambiguity problem by enhancing the learning of features with boundary information. The multiscale feature attention module (MFAM) combines atrous convolution and non-local block to obtain larger receptive fields and long-range contextual information, while the feature fusion module (FFM) balances semantic and spatial information, further improving the embedding of locally discriminative features. Experimental results on ISPRS Potsdam and LoveDA datasets indicate that the proposed MFRNet outperforms other semantic segmentation methods and excels in the accuracy and consistency of object boundary segmentation. [ABSTRACT FROM AUTHOR]