RaHFF-Net: Recall-Adjustable Hierarchical Feature Fusion Network for Remote Sensing Image Change Detection
文献类型：期刊论文
| 作者 | Wang, Bin1,2; Zhao, Kang1,2; Xiao, Tong1,2; Qin, Pinle2; Zeng, Jianchao2 |
| 刊名 | IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
 |
| 出版日期 | 2025 |
| 卷号 | 18页码:176-190 |
| 关键词 | Feature extraction Transformers Semantics Lighting Data mining Tensors Correlation Noise Indexes Adaptation models Change detection (CD) hyperexpectation push pull (HEPP) loss multiscale feature fusion transformer |
| ISSN号 | 1939-1404 |
| DOI | 10.1109/JSTARS.2024.3485687 |
| 产权排序 | 2 |
| 文献子类 | Article |
| 英文摘要 | 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. |
| URL标识 | 查看原文 |
| WOS研究方向 | Engineering ; Physical Geography ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:001409622100016 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/212390]  |
| 专题 | 资源与环境信息系统国家重点实验室_外文论文 |
| 通讯作者 | Qin, Pinle |
| 作者单位 | 1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China 2.North Univ China, Dept Comp Sci & Technol, Taiyuan 030051, Peoples R China; |
| 推荐引用方式 GB/T 7714 | Wang, Bin,Zhao, Kang,Xiao, Tong,et al. RaHFF-Net: Recall-Adjustable Hierarchical Feature Fusion Network for Remote Sensing Image Change Detection[J]. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,2025,18:176-190. |
| APA | Wang, Bin,Zhao, Kang,Xiao, Tong,Qin, Pinle,&Zeng, Jianchao.(2025).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,18,176-190. |
| MLA | Wang, Bin,et al."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 18(2025):176-190. |
入库方式： OAI收割
来源：地理科学与资源研究所
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