Leveraging Pretrained Priors for Weakly Supervised Semantic Segmentation of Remote Sensing Images

Abstract. Semantic segmentation of remote sensing imagery (RSI) is essential for urban mapping, land-use monitoring, and many other domains. However, pixel-level annotation is expensive, making weakly supervised semantic segmentation (WSSS) that relies on image-level labels an attractive alternative. Pre-trained models provide strong priors from large-scale learned representations, making them beneficial for WSSS. However, when kept frozen, they often produce sparse and misaligned class activation maps (CAMs) due to domain gaps and static inference. We propose a lightweight and efficient framework that integrates CLIP and DINO foundation models to address three challenges: (i) semantic misalignment between generic text prompts and RSI-specific visuals; (ii) static CAM quality; and (iii) incomplete object coverage. Our design includes: (1) a Textual Prototype-Aware Enrichment (TPE) module that builds an RS-specific knowledge base using large language model (LLM)-generated descriptions to enrich text prompts; (2) a Unified Semantic Relation Mining (USR) module that fuses learnable adapter features with CLIP attention and DINO affinity for online CAM refinement; and (3) a Visual Prototype-Aware Enrichment (VPE) module, which maintains momentum visual prototypes to complete regions and sharpen boundaries. By freezing the CLIP and DINO backbones and optimizing only lightweight adapter and decoder modules, the proposed framework reduces the number of trainable parameters while achieving competitive performance. Experimental on iSAID and ISPRS Potsdam datasets demonstrate the effectiveness of the proposed framework, achieving 38.01% mIoU on iSAID dataset and 47.01% mIoU with 66.89% overall accuracy on Potsdam dataset.