Knowledge Commons of Institute of Automation,CAS
MsIFT: Multi-Source Image Fusion Transformer | |
Zhang, Xin1,2; Jiang, Hangzhi1,2; Xu, Nuo1,2; Ni, Lei3; Huo, Chunlei1,2; Pan, Chunhong1,2 | |
发表期刊 | REMOTE SENSING |
2022-08-01 | |
卷号 | 14期号:16页码:19 |
摘要 | Multi-source image fusion is very important for improving image representation ability since its essence relies on the complementarity between multi-source information. However, feature-level image fusion methods based on the convolution neural network are impacted by the spatial misalignment between image pairs, which leads to the semantic bias in merging features and destroys the representation ability of the region-of-interests. In this paper, a novel multi-source image fusion transformer (MsIFT) is proposed. Due to the inherent global attention mechanism of the transformer, the MsIFT has non-local fusion receptive fields, and it is more robust to spatial misalignment. Furthermore, multiple classification-based downstream tasks (e.g., pixel-wise classification, image-wise classification and semantic segmentation) are unified in the proposed MsIFT framework, and the fusion module architecture is shared by different tasks. The MsIFT achieved state-of-the-art performances on the image-wise classification dataset VAIS, semantic segmentation dataset SpaceNet 6 and pixel-wise classification dataset GRSS-DFC-2013. The code and trained model are being released upon the publication of the work. |
关键词 | transformer multi-source image fusion non-local |
DOI | 10.3390/rs14164062 |
关键词[WOS] | SHIP CLASSIFICATION ; LIDAR |
收录类别 | SCI |
语种 | 英语 |
资助项目 | National Natural Science Foundation of China[62071466] ; Fund of National Key Laboratory of Science and Technology on Remote Sensing Information and Imagery Analysis, Beijing Research Institute of Uranium Geology[6142A010402] ; Guangxi Natural Science Foundation[2018GXNSFBA281086] |
项目资助者 | National Natural Science Foundation of China ; Fund of National Key Laboratory of Science and Technology on Remote Sensing Information and Imagery Analysis, Beijing Research Institute of Uranium Geology ; Guangxi Natural Science Foundation |
WOS研究方向 | Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology |
WOS类目 | Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology |
WOS记录号 | WOS:000845420000001 |
出版者 | MDPI |
七大方向——子方向分类 | 目标检测、跟踪与识别 |
国重实验室规划方向分类 | 视觉信息处理 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/50033 |
专题 | 多模态人工智能系统全国重点实验室_先进时空数据分析与学习 |
通讯作者 | Huo, Chunlei |
作者单位 | 1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 101408, Peoples R China 3.Beijing Inst Remote Sensing, Beijing 100085, Peoples R China |
第一作者单位 | 模式识别国家重点实验室 |
通讯作者单位 | 模式识别国家重点实验室 |
推荐引用方式 GB/T 7714 | Zhang, Xin,Jiang, Hangzhi,Xu, Nuo,et al. MsIFT: Multi-Source Image Fusion Transformer[J]. REMOTE SENSING,2022,14(16):19. |
APA | Zhang, Xin,Jiang, Hangzhi,Xu, Nuo,Ni, Lei,Huo, Chunlei,&Pan, Chunhong.(2022).MsIFT: Multi-Source Image Fusion Transformer.REMOTE SENSING,14(16),19. |
MLA | Zhang, Xin,et al."MsIFT: Multi-Source Image Fusion Transformer".REMOTE SENSING 14.16(2022):19. |
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文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
remotesensing-14-040(4788KB) | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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