Hierarchical State Space Model for Semantic-Oriented Remote Sensing Image Super-Resolution

Abstract

In recent years, deep learning has driven remarkable progress in remote sensing image super resolution (RSISR). However, super-resolved images do not perform as well as original high-resolution images when directly used in remote sensing classification tasks, due to the loss of semantic information during the reconstruction process. To address this, we focus on semantic-oriented RSISR to improve the remote sensing classification performance using super-resolved images. We propose a dual-branch framework consisting of a semantic enhancement branch and an image reconstruction branch. The semantic enhancement branch exploits pretrained remote sensing classification networks to extract high-level semantic features, which are integrated into the reconstruction process. The reconstruction branch is built upon a novel hierarchical state space model, termed HierMamba, composed of hierarchical state space groups (HSSGs) to achieve multiscale and multigranularity feature extraction. By jointly leveraging semantic priors and HierMamba-based reconstruction, our method generates super-resolved images that are more favorable for downstream classification. Extensive experiments on RSISR benchmarks show that the proposed method produces superior visual quality and consistently improves classification performance compared with existing state-of-the-art methods.