Semantic Segmentation of Remote Sensing Images With Deep Information Enhancement

Using semantic segmentation networks to intelligently categorize remote sensing images (RSIs) is essential for urban planning, land use, and environmental monitoring. However, the complexity of the foreground and background in RSIs, along with the multiscale characteristics of segmented objects, poses a significant challenge for the accurate segmentation of multiclass objects. The multi-source data fusion strategy can improve the segmentation accuracy of RSIs by incorporating complementary information. Inspired by this approach and the robust generalization capabilities of foundation models, we propose a novel method that combines depth maps of foundation models reasoning to improve the segmentation accuracy of RSIs. Specifically, we first utilize Depth Anything (DAM) to extract depth information. Next,we employ two lightweight convolutional layers to fuse depth information at the feature level. Finally, we implement U-Net for end-to-end training and prediction. We conducted numerous semantic segmentation experiments on the Vaihingen dataset. The experimental results demonstrate that our method achieves 73.23% a mean cross-union ratio (mIoU) on the Vaihingen dataset, which is 2.54% higher than the baseline. This performance improvement validates the effectiveness of the proposed method.