HiSTENet: History-Integrated Spatial–Temporal Information Extraction Network for Time Series Remote Sensing Image Change Detection
Time series remote sensing images (TSIs) offer essential data for time series remote sensing image change detection with remote sensing technology advances. However, most existing methods focus on bi-temporal images, lacking the exploration of temporal information between images. This presents a sig...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-02-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/17/5/792 |
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| Summary: | Time series remote sensing images (TSIs) offer essential data for time series remote sensing image change detection with remote sensing technology advances. However, most existing methods focus on bi-temporal images, lacking the exploration of temporal information between images. This presents a significant challenge in effectively utilizing the rich spatio-temporal and object information inherent to TSIs. In this work, we propose a History-Integrated Spatial–Temporal Information Extraction Network (HiSTENet), which comprehensively utilize the spatio-temporal information of TSIs to achieve change detection of continuous image pairs. A Spatial-Temporal Relationship Extraction Module is utilized to model the spatio-temporal relationship. Simultaneously, a Historical Integration Module is introduced to fuse the objects’ characteristics across historical temporal images, while leveraging the features of historical images. Furthermore, the Feature Alignment Fusion Module mitigates pseudo changes by computing feature offsets and aligning images in the feature space. Experiments on SpaceNet7 and DynamicEarthNet demonstrate that HiSTENet outperforms other representative methods, achieving a better balance between precision and recall. |
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| ISSN: | 2072-4292 |