ReA-OVCD: Reliability-Aware Open-Vocabulary Change Detection via Semantic and Spatial Refinement

2026-06-18

AI Summary

This work addresses the inherent trade-off in existing open-vocabulary change detection methods between instance-level and pixel-level modeling, which often compromises fine-grained semantic change recognition and spatial consistency, leading to boundary artifacts and unstable responses. To overcome these limitations, the authors propose a training-free, reliability-aware framework that first identifies candidate change regions through semantic discrepancy and then introduces two novel components: Semantic Change Reasoning (SCR) and Boundary-aware Refinement (BCR). These modules jointly model distributional divergence, response variability, and pixel-wise reliability to collaboratively optimize change discrimination. Evaluated on multiple benchmarks—including LEVIR-CD, WHU-CD, DSIFN, and SECOND—the method achieves F1 score improvements of 2.13%–9.75% over state-of-the-art approaches while demonstrating superior computational efficiency.

Abstract

Unlike traditional remote sensing change detection that relies on predefined categories, Open-Vocabulary Change Detection (OVCD) identifies land cover changes flexibly using arbitrary text prompts. However, existing methods suffer from an inherent trade-off when modeling changes: instance-level comparison overlooks fine-grained semantic variations (e.g., partial building extensions), while direct pixel comparison proves unreliable, yielding unstable responses and boundary artifacts due to semantic ambiguity and spatial inconsistency. To this end, we propose an efficient training-free Reliability-Aware Open-Vocabulary Change Detection (ReA-OVCD) framework. It first derives candidate change regions from pixel-wise semantic discrepancies to ensure flexible and detailed localization. To ensure reliability, it subsequently introduces a collaborative refinement strategy to explicitly model change validity from both semantic and spatial perspectives. Specifically, we develop a Semantic Change Reasoning (SCR) module that reassesses changes by jointly analyzing distributional divergence and response variation, enabling the suppression of incidental inconsistencies while preserving reliable semantic shifts. In addition, a Boundary-aware Change Refinement (BCR) module is designed to mitigate artifacts stemming from boundary misalignment and uncertainty through validating whether candidate regions are supported by reliable interior pixels. Extensive experiments across multiple datasets (LEVIR-CD, WHU-CD, DSIFN, and SECOND) demonstrate that our method consistently outperforms state-of-the-art approaches, achieving $\mathrm{F}_{1}^{C}$ improvements of 2.13\% to 9.75\% with higher computational efficiency. The code is publicly available at \https://github.com/Funny0101/ReA-OVCD

Problem

• Open-Vocabulary Change Detection
• Semantic Ambiguity
• Spatial Inconsistency
• Boundary Artifacts
• Fine-grained Semantic Variation

Innovation

• Open-Vocabulary Change Detection
• Reliability-Aware
• Semantic Change Reasoning
• Boundary-aware Refinement
• Training-Free Framework