Multi-Dimensional Manifolds Consistency Regularization for semi-supervised remote sensing semantic segmentation

Semi -supervised semantic segmentation in remote sensing is critical for urban planning, environmental monitoring and disaster response. The high cost and time required for high -quality data annotation limits its wider application. Traditional semi -supervised deep learning methods, which operate in a single dimension, limit model robustness and generalization. Our study addresses this issue by proposing an effective semisupervised learning method. This method improves model robustness and generalization in remote sensing semantic segmentation. We introduce the Multi -Dimensional Manifolds Consistency Regularization (MDMCR) approach. It applies multi -dimensional perturbations to input images and features, expanding the sample library and improving learning efficiency. Our method has been rigorously tested on various datasets. With only 1/8 of the data labeled, it achieved mean Intersection over Union (mIoU) scores of 74.48% on ISPRS Vaihingen and 78.80% on Potsdam. With only 5% labeled data, it reached 49.93% mIoU on DeepGlobe Roads and 57.90% on Massachusetts Roads. These results show the superiority of our method over existing techniques.