GLoP-SAM3: A Dual Postprocessing Framework With Global–Local Prior Fusion and Reliability Gating for Open-Vocabulary Remote Sensing Segmentation

Abstract

Open-vocabulary remote sensing semantic segmentation enables pixel-level recognition of arbitrary semantic categories through text prompts, providing an effective way to reduce the dependence of traditional closed-set remote sensing segmentation on fixed category sets and large-scale annotations. However, existing training-free methods in remote sensing scenarios still mainly rely on local responses and sliding-window inference and thus suffer from insufficient utilization of full-image global semantic priors, unstable local predictions, and unreliable prior propagation. To address these issues, we propose GLoP-SAM3, a dual postprocessing framework for open-vocabulary remote sensing semantic segmentation, whose core idea is to enhance performance through two lightweight postprocessing modules rather than introducing a new backbone or complex trainable components. Specifically, we develop a divergence-aware global–local fusion (DGLF) module, which adaptively integrates full-image semantic priors with local predictions by jointly modeling local–global discrepancy and local confidence, and a reliability-gated prediction refinement (RGPR) module, which suppresses erroneous prior propagation and improves segmentation quality in complex and boundary regions through high-confidence preservation, ambiguous-region enhancement, and prior reliability gating. Built upon SAM3 without any additional training, the proposed method introduces only limited computational overhead while significantly improving prediction consistency and segmentation accuracy. Extensive experiments on eight public remote sensing datasets demonstrate that GLoP-SAM3 consistently improves the baseline and achieves state-of-the-art (SOTA) performance under the training-free setting on representative benchmarks such as OpenEarthMap, validating the favorable balance of the proposed dual postprocessing strategy between efficiency and performance. The code, fixed prompts, and configuration files are available at https://github.com/LiorCohen0901/GLoP-SAM