CFNet: Optimizing Remote Sensing Change Detection Through Content-Aware Enhancement
Change detection is a crucial and widely applied task in remote sensing, aimed at identifying and analyzing changes occurring in the same geographical area over time. Due to variability in acquisition conditions, bitemporal remote sensing images often exhibit significant differences in image style....
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
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| Online Access: | https://ieeexplore.ieee.org/document/11016006/ |
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| author | Fan Wu Sijun Dong Xiaoliang Meng |
| author_facet | Fan Wu Sijun Dong Xiaoliang Meng |
| author_sort | Fan Wu |
| collection | DOAJ |
| description | Change detection is a crucial and widely applied task in remote sensing, aimed at identifying and analyzing changes occurring in the same geographical area over time. Due to variability in acquisition conditions, bitemporal remote sensing images often exhibit significant differences in image style. Even with the powerful generalization capabilities of DNNs, these unpredictable style variations between bitemporal images inevitably affect the model’s ability to accurately detect changed areas. To address issue above, we propose the Content Focuser Network (CFNet), which takes content-aware strategy as a key insight. CFNet employs EfficientNet-B5 as the backbone for feature extraction. To enhance the model’s focus on the content features of images while mitigating the misleading effects of style features, we develop a constraint strategy that prioritizes the content features of bitemporal images, termed Content-Aware. Furthermore, to enable the model to flexibly focus on changed and unchanged areas according to the requirements of different stages, we design a reweighting module based on the cosine distance between bitemporal image features, termed Focuser. CFNet achieve outstanding performance across three well-known change detection datasets: CLCD (F1: 81.41%, IoU: 68.65% ), LEVIR-CD (F1: 92.18%, IoU: 85.49% ), and SYSU-CD (F1: 82.89%, IoU: 70.78% ). |
| format | Article |
| id | doaj-art-a47c48145e0040f5aa61437bd41ace7b |
| institution | OA Journals |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-a47c48145e0040f5aa61437bd41ace7b2025-08-20T02:37:05ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-0118146881470410.1109/JSTARS.2025.357417311016006CFNet: Optimizing Remote Sensing Change Detection Through Content-Aware EnhancementFan Wu0https://orcid.org/0009-0005-4103-9088Sijun Dong1https://orcid.org/0000-0003-4218-6790Xiaoliang Meng2https://orcid.org/0000-0002-3271-9314School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, ChinaSchool of Remote Sensing and Information Engineering, Wuhan University, Wuhan, ChinaSchool of Remote Sensing and Information Engineering, Wuhan University, Wuhan, ChinaChange detection is a crucial and widely applied task in remote sensing, aimed at identifying and analyzing changes occurring in the same geographical area over time. Due to variability in acquisition conditions, bitemporal remote sensing images often exhibit significant differences in image style. Even with the powerful generalization capabilities of DNNs, these unpredictable style variations between bitemporal images inevitably affect the model’s ability to accurately detect changed areas. To address issue above, we propose the Content Focuser Network (CFNet), which takes content-aware strategy as a key insight. CFNet employs EfficientNet-B5 as the backbone for feature extraction. To enhance the model’s focus on the content features of images while mitigating the misleading effects of style features, we develop a constraint strategy that prioritizes the content features of bitemporal images, termed Content-Aware. Furthermore, to enable the model to flexibly focus on changed and unchanged areas according to the requirements of different stages, we design a reweighting module based on the cosine distance between bitemporal image features, termed Focuser. CFNet achieve outstanding performance across three well-known change detection datasets: CLCD (F1: 81.41%, IoU: 68.65% ), LEVIR-CD (F1: 92.18%, IoU: 85.49% ), and SYSU-CD (F1: 82.89%, IoU: 70.78% ).https://ieeexplore.ieee.org/document/11016006/Change detectioncontent-awarefeature focuser |
| spellingShingle | Fan Wu Sijun Dong Xiaoliang Meng CFNet: Optimizing Remote Sensing Change Detection Through Content-Aware Enhancement IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Change detection content-aware feature focuser |
| title | CFNet: Optimizing Remote Sensing Change Detection Through Content-Aware Enhancement |
| title_full | CFNet: Optimizing Remote Sensing Change Detection Through Content-Aware Enhancement |
| title_fullStr | CFNet: Optimizing Remote Sensing Change Detection Through Content-Aware Enhancement |
| title_full_unstemmed | CFNet: Optimizing Remote Sensing Change Detection Through Content-Aware Enhancement |
| title_short | CFNet: Optimizing Remote Sensing Change Detection Through Content-Aware Enhancement |
| title_sort | cfnet optimizing remote sensing change detection through content aware enhancement |
| topic | Change detection content-aware feature focuser |
| url | https://ieeexplore.ieee.org/document/11016006/ |
| work_keys_str_mv | AT fanwu cfnetoptimizingremotesensingchangedetectionthroughcontentawareenhancement AT sijundong cfnetoptimizingremotesensingchangedetectionthroughcontentawareenhancement AT xiaoliangmeng cfnetoptimizingremotesensingchangedetectionthroughcontentawareenhancement |