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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| Format: | Article |
| Language: | English |
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MDPI AG
2025-02-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/5/792 |
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| author | Lu Zhao Ling Wan Lei Ma Yiming Zhang |
| author_facet | Lu Zhao Ling Wan Lei Ma Yiming Zhang |
| author_sort | Lu Zhao |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-1aa9520d47484a77bf182df055c5e9d1 |
| institution | OA Journals |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-1aa9520d47484a77bf182df055c5e9d12025-08-20T02:06:13ZengMDPI AGRemote Sensing2072-42922025-02-0117579210.3390/rs17050792HiSTENet: History-Integrated Spatial–Temporal Information Extraction Network for Time Series Remote Sensing Image Change DetectionLu Zhao0Ling Wan1Lei Ma2Yiming Zhang3Institute of Automation, Chinese Academy of Sciences, Beijing 100190, ChinaInstitute of Automation, Chinese Academy of Sciences, Beijing 100190, ChinaInstitute of Automation, Chinese Academy of Sciences, Beijing 100190, ChinaBeijing Institute of Remote Sensing Information, Beijing 100011, ChinaTime 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.https://www.mdpi.com/2072-4292/17/5/792time series remote sensing imagestime series remote sensing images change detectionspatial–temporal relationshipfeature fusiondeep learning |
| spellingShingle | Lu Zhao Ling Wan Lei Ma Yiming Zhang HiSTENet: History-Integrated Spatial–Temporal Information Extraction Network for Time Series Remote Sensing Image Change Detection Remote Sensing time series remote sensing images time series remote sensing images change detection spatial–temporal relationship feature fusion deep learning |
| title | HiSTENet: History-Integrated Spatial–Temporal Information Extraction Network for Time Series Remote Sensing Image Change Detection |
| title_full | HiSTENet: History-Integrated Spatial–Temporal Information Extraction Network for Time Series Remote Sensing Image Change Detection |
| title_fullStr | HiSTENet: History-Integrated Spatial–Temporal Information Extraction Network for Time Series Remote Sensing Image Change Detection |
| title_full_unstemmed | HiSTENet: History-Integrated Spatial–Temporal Information Extraction Network for Time Series Remote Sensing Image Change Detection |
| title_short | HiSTENet: History-Integrated Spatial–Temporal Information Extraction Network for Time Series Remote Sensing Image Change Detection |
| title_sort | histenet history integrated spatial temporal information extraction network for time series remote sensing image change detection |
| topic | time series remote sensing images time series remote sensing images change detection spatial–temporal relationship feature fusion deep learning |
| url | https://www.mdpi.com/2072-4292/17/5/792 |
| work_keys_str_mv | AT luzhao histenethistoryintegratedspatialtemporalinformationextractionnetworkfortimeseriesremotesensingimagechangedetection AT lingwan histenethistoryintegratedspatialtemporalinformationextractionnetworkfortimeseriesremotesensingimagechangedetection AT leima histenethistoryintegratedspatialtemporalinformationextractionnetworkfortimeseriesremotesensingimagechangedetection AT yimingzhang histenethistoryintegratedspatialtemporalinformationextractionnetworkfortimeseriesremotesensingimagechangedetection |