Abstract: Remote sensing image segmentation plays an important role in urban planning, land resource management, traffic planning, and other fields. But the complex environment and large differences in the scale of remote sensing images bring some difficulties to the segmentation of remote sensing images. A feature fusion module combined with channel attention is proposed to replace the decoder in the Deeplabv3+ network to adaptively select useful spatial detail features from low-level features guided by high-level features, and screen the relevant interference information. Among them, the weighted high-level features are obtained through the channel attention mechanism, which is conducive to the extraction of global contexts and more effective semantic information. To retain more image edge, texture, and other information the weighted high-level features are used to guide the extraction of refined low-level feature information. Training and testing are carried out on the INRIA Aerial Image high-resolution remote sensing image data set, and compared with related models. The experimental results show that the improved Deeplabv3+ network has an excellent performance in remote sensing image segmentation, improving the segmentation effect of target edge and small-scale objects, which has certain research and application value.