CIBENet: A channel interaction and bridging-enhanced change detection network for optical and SAR remote sensing images
文献类型：期刊论文
| 作者 | Huang, Liang1,3; Wang, Min3; Tang, Bo-Hui1,2,3; Le, Weipeng3; Tian, Qiuyuan3 |
| 刊名 | INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION
 |
| 出版日期 | 2024-07-01 |
| 卷号 | 131页码:12 |
| 关键词 | Heterogeneous images Bridging -decoder Change detection Deep transformation Deep learning |
| ISSN号 | 1569-8432 |
| DOI | 10.1016/j.jag.2024.103969 |
| 英文摘要 | Effectively utilizing the complementary characteristics between optical and SAR remote sensing images to accurately identify change information is of great practical significance. Direct pixel comparisons are challenging since they come from sensors with different imaging mechanisms. Therefore, in this paper, a novel domain transformation that incorporates c hannel i nteraction and a b ridging - e nhanced (CIBENet) heterogeneous change detection net work is proposed, where twin-depthwise separable fused gated channel transformation is the convolutional block designed for channel information interaction. And samples without changed semantic information are used to train the deep transformation model, which effectively solves the interference of semantic information on NiceGAN. A different perspective is provided for the heterogeneous change detection task. The backend change detection network takes UNet ++ with twin-depthwise separable convolution as the baseline, introduces the gated channel transformation and bridging -enhanced decoder, and models the feature relationship between channels to strengthen the channel information interaction while suppressing the expression of nonchanged information. In addition, the bridging -enhanced decoder can efficiently solve localized holes and discontinuities in binary maps by bridging identical pixels. CIBENet is supervised and experimented on three heterogeneous change detection datasets, Gloucester, Shuguang, and Italy, also compared with the classical unsupervised methods CGAN, SCCN, INLPG and advanced supervised methods DTCDN, DACDT. The network model proposed in this paper significantly improved F1 and recall, and the overall accuracies were 96.60 %, 98.23 %, and 96.29 %, respectively. The experiments validated the reliability of the proposed model. |
| 资助项目 | National Natural Science Foundation of China[42361054] ; National Natural Science Foundation of China[42230109] ; Yunnan Fundamental Research Projects[202201AT070164] ; Hunan Provincial Natural Science Foundation of China[2023JJ60561] ; Yunnan Province key research and development program[202202AD080010] ; The Xingdian talent support program project |
| WOS研究方向 | Remote Sensing |
| 语种 | 英语 |
| WOS记录号 | WOS:001254997800001 |
| 出版者 | ELSEVIER |
| 资助机构 | National Natural Science Foundation of China ; Yunnan Fundamental Research Projects ; Hunan Provincial Natural Science Foundation of China ; Yunnan Province key research and development program ; The Xingdian talent support program project |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/206191]  |
| 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 | Wang, Min |
| 作者单位 | 1.Yunnan Prov Dept Educ, Key Lab Plateau Remote Sensing, Kunming 650093, Peoples R China 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China 3.Kunming Univ Sci & Technol, Fac Land & Resources Engn, Kunming 650093, Peoples R China |
| 推荐引用方式 GB/T 7714 | Huang, Liang,Wang, Min,Tang, Bo-Hui,et al. CIBENet: A channel interaction and bridging-enhanced change detection network for optical and SAR remote sensing images[J]. INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION,2024,131:12. |
| APA | Huang, Liang,Wang, Min,Tang, Bo-Hui,Le, Weipeng,&Tian, Qiuyuan.(2024).CIBENet: A channel interaction and bridging-enhanced change detection network for optical and SAR remote sensing images.INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION,131,12. |
| MLA | Huang, Liang,et al."CIBENet: A channel interaction and bridging-enhanced change detection network for optical and SAR remote sensing images".INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 131(2024):12. |
入库方式： OAI收割
来源：地理科学与资源研究所
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