SCNAnet: Structure-Aware Contrastive with Noise-Augmented Network for Unsupervised Change Detection.
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| Title: | SCNAnet: Structure-Aware Contrastive with Noise-Augmented Network for Unsupervised Change Detection. |
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| Authors: | Sun, Yijie1 (AUTHOR), Wu, Qingxi2 (AUTHOR), Wang, Nan1,2 (AUTHOR) wang_nan@bit.edu.cn |
| Source: | Remote Sensing. May2026, Vol. 18 Issue 9, p1427. 23p. |
| Subjects: | Remote sensing, Data augmentation, Change-point problems, Contrastive learning, Encoding |
| Abstract: | Highlights: What are the main findings? We propose SCNAnet, an unsupervised change detection framework that combines a structure-aware style encoder, a noise-perturbation consistency branch, and a frequency-attention decoder to reduce shortcut-driven optimization and improve semantic change representation. SCNAnet achieves state-of-the-art performance on GF-2 VHR, OSCD, and QuickBird datasets, with more accurate change localization, fewer false positives, and clearer boundaries than competing unsupervised methods. What are the implications of the main findings? The results show that alleviating optimization shortcuts is critical for unsupervised remote sensing change detection, because style-loss-driven training alone may misclassify unchanged but stylistically similar regions as changes. The proposed framework provides a practical way to improve robustness in complex scenarios with seasonal variation, illumination differences, and multi-scale changes, which is valuable for real-world Earth observation applications. Unsupervised change detection (UCD) is a key technique in Earth observation, aiming to identify and quantify surface changes over time by analyzing multi-temporal remote sensing images without manual annotations. Unlike supervised approaches that rely on ground reference to directly guide discriminative semantic learning, UCD methods must construct their own reference. A mainstream strategy employs one temporal image as the reference and uses transformation models (e.g., style transfer networks) to align the other image in unchanged regions. Loss is then reduced by labeling hard-to-align pixels as "changes" and excluding them from the objective. However, this optimization process is dominated by style losses, which cause the model to learn to exclude regions that make only limited contributions to style-loss minimization, rather than to acquire discriminative representations of true geospatial changes. Such shortcut-driven optimization results in insufficient modeling of genuine change features and frequent misclassification of unchanged yet stylistically similar regions. To address these limitations, we propose SCNAnet, a novel framework that integrates three modules: a noise-perturbation consistency branch to suppress shortcut-driven learning, a structure-aware style transformation encoder to strengthen semantic representations of structural changes, and a frequency-attention decoder to refine the delineation of change regions. Extensive experiments on three benchmark datasets (GF-2, OSCD, and QuickBird) demonstrate the effectiveness of SCNAnet. Specifically, SCNAnet improves the F1 score by approximately 8% on the Montpellier dataset compared with the second-best method, demonstrating its effectiveness under challenging conditions. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? We propose SCNAnet, an unsupervised change detection framework that combines a structure-aware style encoder, a noise-perturbation consistency branch, and a frequency-attention decoder to reduce shortcut-driven optimization and improve semantic change representation. SCNAnet achieves state-of-the-art performance on GF-2 VHR, OSCD, and QuickBird datasets, with more accurate change localization, fewer false positives, and clearer boundaries than competing unsupervised methods. What are the implications of the main findings? The results show that alleviating optimization shortcuts is critical for unsupervised remote sensing change detection, because style-loss-driven training alone may misclassify unchanged but stylistically similar regions as changes. The proposed framework provides a practical way to improve robustness in complex scenarios with seasonal variation, illumination differences, and multi-scale changes, which is valuable for real-world Earth observation applications. Unsupervised change detection (UCD) is a key technique in Earth observation, aiming to identify and quantify surface changes over time by analyzing multi-temporal remote sensing images without manual annotations. Unlike supervised approaches that rely on ground reference to directly guide discriminative semantic learning, UCD methods must construct their own reference. A mainstream strategy employs one temporal image as the reference and uses transformation models (e.g., style transfer networks) to align the other image in unchanged regions. Loss is then reduced by labeling hard-to-align pixels as "changes" and excluding them from the objective. However, this optimization process is dominated by style losses, which cause the model to learn to exclude regions that make only limited contributions to style-loss minimization, rather than to acquire discriminative representations of true geospatial changes. Such shortcut-driven optimization results in insufficient modeling of genuine change features and frequent misclassification of unchanged yet stylistically similar regions. To address these limitations, we propose SCNAnet, a novel framework that integrates three modules: a noise-perturbation consistency branch to suppress shortcut-driven learning, a structure-aware style transformation encoder to strengthen semantic representations of structural changes, and a frequency-attention decoder to refine the delineation of change regions. Extensive experiments on three benchmark datasets (GF-2, OSCD, and QuickBird) demonstrate the effectiveness of SCNAnet. Specifically, SCNAnet improves the F1 score by approximately 8% on the Montpellier dataset compared with the second-best method, demonstrating its effectiveness under challenging conditions. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18091427 |
