Work

Detecting semantic changes from VHR remote sensing images by integrating semantic correlations and change priors

Year: 2025

Type: article

Abstract: Investigating semantic change detection (SCD) is critical for tracking the evolution of urban and rural conversion. However, existing methods applied to SCD face two pivotal challenges: (1) modeling of semantic correlations among bi-temporal features is insufficient, leading to substantial pseudo-changes induced by seasonal and temporal differences; and (2) change information lacks effective enhancement, causing incomplete and missed detection. To overcome these challenges, we propose a change prior-guided network, namely CPGNet, which employs a multi-branch paradigm that integrates supplemental changed information to accurately identify diverse types of land cover changes in very high-resolution (VHR) remote sensing images. Initially, a Siamese network is introduced to simultaneously extract robust semantic representations from bi-temporal images. Next, a semantic correlation module is designed to capture and process the interconnections present in bi-temporal features, thereby alleviating pseudo-changes resulting from season variance and time disparities. We then incorporate an auxiliary CD branch to capture changed prior information. Finally, a multi-view change feature fusion module based on Bayesian theory is developed to enhance salient changed features to improve the under-detection of changed areas, particularly for changed objects with small sizes and complex shapes. We applied the proposed method to the semantic change detection dataset (SECOND), JiLin-1 (JL-1) dataset, and Xiamen (XM) dataset, and compared the results to ten typical SCD methods. Our method yielded superior accuracy, outperforming existing methods by 5.97%–40.54%, 9.28%–65.98%, and 0.99%–13.77% in SeK value on the JL-1, XM, and SECOND datasets, respectively. Moreover, the detected results exhibited well boundary consistency and internal integrity. Our data and code are accessible at https://github.com/long123524/CPGNet . • CPGNet is developed to detect semantic changes from VHR remote sensing images. • An auxiliary change detection branch is used to provide changed prior information. • An SCM for modeling semantic correlations between bi-temporal features. • The proposed MCFF module improves the under-detection of changed areas. • CPGNet performs better than ten existing methods across varied changed scenarios.

Source: International Journal of Applied Earth Observation and Geoinformation

DOI: 10.1016/j.jag.2025.104916

Authors: Jiang Long, Hongwei Zeng, Hang Zhao, Haihan Lin, Junbin Li

Institutions: Tongji University, Chinese Academy of Sciences, Aerospace Information Research Institute, University of Chinese Academy of Sciences, Fuzhou University

Language: English

Topic: Remote-Sensing Image Classification

Subfield: Media Technology

Field: Engineering

Domain: Physical Sciences

SDGs: Sustainable cities and communities, Climate action

Open Access status: gold

Estimated APC paid: $3,550