RaHFF-Net: Recall-Adjustable Hierarchical Feature Fusion Network for Remote Sensing Image Change Detection
Remote sensing (RS) image change detection (CD) aims to identify areas of interest that have changed between bitemporal images. For complex scenarios (e.g., varying lighting conditions), the diverse shapes and scales of the changed areas is especially vulnerable to cause CD models to suffer from ser...
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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/10733986/ |
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| author | Bin Wang Kang Zhao Tong Xiao Pinle Qin Jianchao Zeng |
| author_facet | Bin Wang Kang Zhao Tong Xiao Pinle Qin Jianchao Zeng |
| author_sort | Bin Wang |
| collection | DOAJ |
| description | Remote sensing (RS) image change detection (CD) aims to identify areas of interest that have changed between bitemporal images. For complex scenarios (e.g., varying lighting conditions), the diverse shapes and scales of the changed areas is especially vulnerable to cause CD models to suffer from serious missed detections. To address aforementioned problem, we propose a high recall multiscale feature fusion model for RS change interpretation. Initially, the RaHFF-Net extracts hierarchical multiscale feature from bitemporal RS images; Then, it employs CNN and Transformer to effectively merge local and global information across same-scale, cross-scale, and multiscale features. Finally, to address the issue of instance imbalance in CD, a novel hyperexpectation push pull loss regularization term is proposed. This loss function is designed to elevate the expected predictions of positive instances across the dataset, thereby enabling the development of a deep learning model with a high recall rate. |
| format | Article |
| id | doaj-art-fc971e66707b4c7eb6b263e0be6f5c5b |
| institution | Kabale University |
| 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-fc971e66707b4c7eb6b263e0be6f5c5b2024-11-26T00:00:16ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-011817619010.1109/JSTARS.2024.348568710733986RaHFF-Net: Recall-Adjustable Hierarchical Feature Fusion Network for Remote Sensing Image Change DetectionBin Wang0https://orcid.org/0000-0002-3372-7906Kang Zhao1https://orcid.org/0009-0006-5342-7376Tong Xiao2https://orcid.org/0009-0009-3626-5186Pinle Qin3https://orcid.org/0000-0002-9086-7366Jianchao Zeng4https://orcid.org/0000-0001-7755-7550Department of Computer Science and Technology, North University of China, Taiyuan, ChinaDepartment of Computer Science and Technology, North University of China, Taiyuan, ChinaDepartment of Computer Science and Technology, North University of China, Taiyuan, ChinaDepartment of Computer Science and Technology, North University of China, Taiyuan, ChinaDepartment of Computer Science and Technology, North University of China, Taiyuan, ChinaRemote sensing (RS) image change detection (CD) aims to identify areas of interest that have changed between bitemporal images. For complex scenarios (e.g., varying lighting conditions), the diverse shapes and scales of the changed areas is especially vulnerable to cause CD models to suffer from serious missed detections. To address aforementioned problem, we propose a high recall multiscale feature fusion model for RS change interpretation. Initially, the RaHFF-Net extracts hierarchical multiscale feature from bitemporal RS images; Then, it employs CNN and Transformer to effectively merge local and global information across same-scale, cross-scale, and multiscale features. Finally, to address the issue of instance imbalance in CD, a novel hyperexpectation push pull loss regularization term is proposed. This loss function is designed to elevate the expected predictions of positive instances across the dataset, thereby enabling the development of a deep learning model with a high recall rate.https://ieeexplore.ieee.org/document/10733986/Change detection (CD)hyperexpectation push pull (HEPP) lossmultiscale feature fusiontransformer |
| spellingShingle | Bin Wang Kang Zhao Tong Xiao Pinle Qin Jianchao Zeng RaHFF-Net: Recall-Adjustable Hierarchical Feature Fusion Network for Remote Sensing Image Change Detection IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Change detection (CD) hyperexpectation push pull (HEPP) loss multiscale feature fusion transformer |
| title | RaHFF-Net: Recall-Adjustable Hierarchical Feature Fusion Network for Remote Sensing Image Change Detection |
| title_full | RaHFF-Net: Recall-Adjustable Hierarchical Feature Fusion Network for Remote Sensing Image Change Detection |
| title_fullStr | RaHFF-Net: Recall-Adjustable Hierarchical Feature Fusion Network for Remote Sensing Image Change Detection |
| title_full_unstemmed | RaHFF-Net: Recall-Adjustable Hierarchical Feature Fusion Network for Remote Sensing Image Change Detection |
| title_short | RaHFF-Net: Recall-Adjustable Hierarchical Feature Fusion Network for Remote Sensing Image Change Detection |
| title_sort | rahff net recall adjustable hierarchical feature fusion network for remote sensing image change detection |
| topic | Change detection (CD) hyperexpectation push pull (HEPP) loss multiscale feature fusion transformer |
| url | https://ieeexplore.ieee.org/document/10733986/ |
| work_keys_str_mv | AT binwang rahffnetrecalladjustablehierarchicalfeaturefusionnetworkforremotesensingimagechangedetection AT kangzhao rahffnetrecalladjustablehierarchicalfeaturefusionnetworkforremotesensingimagechangedetection AT tongxiao rahffnetrecalladjustablehierarchicalfeaturefusionnetworkforremotesensingimagechangedetection AT pinleqin rahffnetrecalladjustablehierarchicalfeaturefusionnetworkforremotesensingimagechangedetection AT jianchaozeng rahffnetrecalladjustablehierarchicalfeaturefusionnetworkforremotesensingimagechangedetection |