Domain-Incremental Learning for Remote Sensing Semantic Segmentation With Multifeature Constraints in Graph Space

The use of deep learning techniques for semantic segmentation in remote sensing has been increasingly prevalent. Effectively modeling remote contextual information and integrating high-level abstract features with low-level spatial features are critical challenges for semantic segmentation tasks. This article addresses these challenges by constructing a graph space reasoning (GSR) module and a dual-channel cross-attention upsampling (DCAU) module. Meanwhile, a new domain-incremental learning (DIL) framework is designed to alleviate catastrophic forgetting when the deep learning model is used in cross-domain. This framework makes a balance between retaining prior knowledge and acquiring new information through the use of frozen feature layers and multifeature joint loss optimization. Based on this, a new DIL of remote sensing semantic segmentation with multifeature constraints in graph space (GSMF-RS-DIL) framework is proposed. Extensive experiments, including ablation experiments on the ISPRS and LoveDA datasets, demonstrate that the proposed method achieves superior performance and optimal computational efficiency in both single-domain and cross-domain tasks. The code is publicly available at https://github.com/Huang WBill/GSMF-RS-DIL.