SegMind: Semisupervised Remote Sensing Image Semantic Segmentation With Masked Image Modeling and Contrastive Learning Method
Zhenghong Li, Hao Chen, Jiangjiang Wu, Jun Li, Ning Jing
Abstract
Remote sensing (RS) image semantic segmentation has attracted much attention due to its wide applications. However, deep learning based RS image semantic segmentation methods usually require substantial manual pixel-wise annotations, which are expensive and hard to obtain in practice. Although existing semi-supervised RS semantic segmentation methods effectively reduce dependence on labeled data, they generally focus on information consistency between labeled and unlabeled images, but ignore the potential context information between different areas of the RS image. In fact, the objects contained in a RS image usually have some long-range dependence between each other, since trees are usually on both sides of a road, and the middle of two rows of houses is commonly a road. Therefore, we believe that the potential dependencies between different areas of the RS image should be beneficial for reducing the label dependence of RS semantic segmentation. Based on this point, we propose a novel semi-supervised RS image semantic segmentation network named SegMind, which is based on mean teacher (MT) architecture and adopts Masked Image Modeling (MIM) to enhance information interactions of different areas. Moreover, Contrastive Learning (CL) and entropy loss are introduced to SegMind framework to further improve the linear separability and prediction confidence of the proposed model. Experiments on three datasets have demonstrated the superiority of proposed method over the state-of-the-art methods. The code is available at https://github.com/lzh-ggs-ddu/SegMind.