Next Article in Journal
FireMambaNet: A Multi-Scale Mamba Network for Tiny Fire Segmentation in Satellite Imagery
Next Article in Special Issue
A Feature-Optimized Deep Learning Framework for Mapping and Spatial Characterization of Tea Plantations in Complex Mountain Landscapes
Previous Article in Journal
RShDet: An Adaptive Spectral-Aware Network for Remote Sensing Object Detection Under Haze Corruption
Previous Article in Special Issue
GIMMNet: Geometry-Aware Interactive Multi-Modal Network for Semantic Segmentation of High-Resolution Remote Sensing Imagery
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Unsupervised Change Detection in Heterogeneous Remote Sensing Images via Dynamic Mask Guidance

1
School of Electrical Engineering and Intelligentization, Dongguan University of Technology, Dongguan 523808, China
2
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519080, China
3
School of Artificial Intelligence, Sun Yat-sen University, Zhuhai 519089, China
4
School of Intelligent Manufacturing and Electrical Engineering, Guangzhou Institute of Science and Technology, Guangzhou 510640, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2026, 18(7), 1022; https://doi.org/10.3390/rs18071022
Submission received: 2 February 2026 / Revised: 24 March 2026 / Accepted: 27 March 2026 / Published: 29 March 2026

Abstract

Unsupervised change detection (CD) in heterogeneous remote sensing images is intrinsically difficult due to severe sensor-specific discrepancies. In the absence of ground truth, these discrepancies result in ambiguous optimization objectives that make it difficult for models to distinguish true land-cover changes from modality-driven pseudo-changes. To address these challenges, we propose MaskUCD, a novel unsupervised framework that reformulates heterogeneous CD as a dynamic mask-driven constraint scheduling problem. Fundamentally distinct from conventional strategies that enforce selective feature alignment, MaskUCD employs a spatially adaptive optimization mechanism. Specifically, the iteratively refined mask serves as a geometric reference to guide optimization. It enforces strict feature alignment in mask-unchanged regions to suppress modality-induced discrepancies, while simultaneously promoting feature divergence in mask-changed regions to emphasize semantic inconsistencies. In this way, explicit optimization objectives are established, together with an intrinsic interpretability constraint that guides the CD process. This strategy treats the mask as a structural guide for representation learning rather than a ground-truth reference, thereby avoiding error accumulation caused by directly using inaccurate masks as supervisory signals. To facilitate this optimization, we design a specialized asymmetric autoencoder with a hybrid encoder architecture, utilizing multi-scale frequency analysis and global context modeling to enhance feature representation capabilities. Consequently, this design enables the generation of refined and semantically consistent masks, which provide increasingly precise structural guidance, yielding converged and discriminative difference maps. Extensive experiments demonstrate that MaskUCD achieves state-of-the-art performance and superior robustness compared to existing advanced methods.
Keywords: remote sensing; heterogeneous change detection; mask guidance strategy; unsupervised learning remote sensing; heterogeneous change detection; mask guidance strategy; unsupervised learning

Share and Cite

MDPI and ACS Style

Xie, P.; Chen, G.; Zhou, Q.; Li, X.; Yan, J. Unsupervised Change Detection in Heterogeneous Remote Sensing Images via Dynamic Mask Guidance. Remote Sens. 2026, 18, 1022. https://doi.org/10.3390/rs18071022

AMA Style

Xie P, Chen G, Zhou Q, Li X, Yan J. Unsupervised Change Detection in Heterogeneous Remote Sensing Images via Dynamic Mask Guidance. Remote Sensing. 2026; 18(7):1022. https://doi.org/10.3390/rs18071022

Chicago/Turabian Style

Xie, Paixin, Gao Chen, Qingfeng Zhou, Xiaoyan Li, and Jingwen Yan. 2026. "Unsupervised Change Detection in Heterogeneous Remote Sensing Images via Dynamic Mask Guidance" Remote Sensing 18, no. 7: 1022. https://doi.org/10.3390/rs18071022

APA Style

Xie, P., Chen, G., Zhou, Q., Li, X., & Yan, J. (2026). Unsupervised Change Detection in Heterogeneous Remote Sensing Images via Dynamic Mask Guidance. Remote Sensing, 18(7), 1022. https://doi.org/10.3390/rs18071022

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop