遥感图像因具有丰富的语义信息和空间信息，增加了语义分割的难度。然而已有提取双模态特征的分割方法采用相同的主干网络，没有考虑互补特征的差异，存在特征提取、特征融合和上采样恢复细节信息不足等问题，无法准确高效的学习高分辨率遥感图像信息。因此，本文提出基于双模态高效特征学习的高分辨率遥感图像分割算法。首先，针对不同模态的遥感图像设计合适的编码器，高效的提取双模态特征，并通过交互加强模块减少不同路径特征之间的差异。其次，提出双模态特征聚合模块和深层特征提取模块进一步融合和提取双模态特征，使网络能够充分学习互补信息。最后，提出多层特征上采样模块，利用语义信息丰富的高层特征对细节信息丰富的低层特征进行加权操作，逐步上采样实现特征高效恢复，提升分割性能。实验结果表明，所提算法在ISPRS Potsdam和Vaihingen数据集上的总体精度分别达到了94.52%、90.45%，能够高效的提取并融合高分辨率遥感图像的双模态特征，提高遥感图像分割的准确率。

With the rapid development of spatial technology, the resolution of remote sensing images is gradually im-proved, and the detailed information and spatial information contained in remote sensing images are also richer, which leads to the problem that the difference between different categories becomes smaller and the difference between the same categories becomes larger, that is, the phenomenon of the same spectrum of foreign objects and the different spectrum of the same objects is serious. However, the existing dual-modal segmentation methods do not extract the dual-modal feature information of remote sensing images sepa-rately, and the fusion features are not sufficient, and the details of upsampling recovery are insufficient, resulting in the inability to accurately and efficiently learn remote sensing image information, resulting in segmentation errors, edge blur and other problems. This study proposes a high-resolution remote sensing image segmentation based on dual-modal effi-cient feature learning. The algorithm designs appropriate encoders for different modal remote sensing im-ages, efficiently extracts dual-modal features, and reduces the differences between different path features through interactive reinforcement modules. Then, the dual-modal feature aggregation module and the deep feature extraction module are proposed to further fuse and extract the dual-modal features, so that the net-work can fully learn the complementary information of the dual-modal. Finally, a multi-layer feature up-sampling module is proposed, which uses the high-level features with rich semantic information to weight the low-level features with rich detail information, and gradually upsampling to achieve efficient feature recovery and improve segmentation performance. In this paper, experimental on the Potsdam and Vaihingen datasets demonstrate the overall accuracy reaches 94.52 % and 90.45 % respectively. The experimental results show that the segmentation effect of the proposed algorithm is better than that of the existing algorithms, which can efficiently extract and fuse the multi-modal complementary features of high-resolution remote sensing images, and improve the seg-mentation accuracy of remote sensing images. This study proposes a high-resolution remote sensing image segmentation based on dual-modal effi-cient feature learning. Experiments on ISPRS Potsdam and Vaihingen datasets show that the proposed model is more suitable for segmenting low vegetation and trees, buildings and roads with very similar spectral features, and can also achieve accurate segmentation of small targets such as cars. However, the complexity of the model needs to be further reduced, and there is still much room for improvement in ac-curacy. In the future, a better segmentation network will be designed to fuse more than two modal features to obtain more feature information to achieve more accurate remote-sensing image segmentation.