The current high-resolution remote sensing image segmentation model based on deep learning has the problems of high delay and low response caused by a large number of parameters and complex calculations. Considering the problems, this study proposes a lightweight remote sensing feature segmentation method, which can better balance speed and accuracy. This method uses MobileNetV2 for rough feature extraction, constructs spatial information embedding branches to achieve fine feature extraction on different scales, and introduces dense connections between different levels to obtain dense contextual information. The decoding end designs the feature fusion optimization strategy to fuse the features of different scales layer by layer to increase the perception of fine-grained features. Meanwhile, upsampling with alternating deconvolution and bilinear interpolation is employed to reduce the image edge information loss. Finally, the cross-entropy loss is combined with the Dice loss to accelerate network convergence. Comparative experiments are carried out with several commonly used semantic segmentation methods to verify the effectiveness of the proposed method. The experimental results show that the segmentation accuracy of the proposed algorithm is 93.7%, and the MIoU is 88.01%, which can achieve effective segmentation of ground objects.