Cross-Domain Scene Classification by Integrating Multiple Incomplete Sources | |
Gong, Tengfei1,2; Zheng, Xiangtao1; Lu, Xiaoqiang1 | |
作者部门 | 光谱成像技术研究室 |
2021-12 | |
发表期刊 | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING |
ISSN | 0196-2892;1558-0644 |
卷号 | 59期号:12页码:10035-10046 |
产权排序 | 1 |
摘要 | Cross-domain scene classification identifies scene categories by learning knowledge from a labeled data set (source domain) to an unlabeled data set (target domain), where the source data and the target data are sampled from different distributions. A lot of domain adaptation methods are used to reduce the distribution shift across domains, and most existing methods assume that the source domain shares the same categories with the target domain. It is usually hard to find a source domain that covers all categories in the target domain. Some works exploit multiple incomplete source domains to cover the target domain. However, in such setting, the categories of each source domain are a subset of the target-domain categories, and the target domain contains unknown categories for each source domain. The existence of unknown categories results in the conventional domain adaptation unsuitable. Known and unknown categories should be treated separately. Therefore, a separation mechanism is proposed to separate the known and unknown categories in this article. First, multiple-source classifiers trained on the multiple source domains are used to coarsely separate the known/unknown categories in the target domain. The target images with high similarities to source images are selected as known categories, and the target images with low similarities are selected as unknown categories. Then, a binary classifier trained using the selected images is used to finely separate all target-domain images. Finally, only the known categories are implemented in the cross-domain alignment and classification. The target images get labels by integrating the hypotheses of multiple-source classifiers on the known categories. Experiments are conducted on three cross-domain data sets to demonstrate the effectiveness of the proposed method. |
关键词 | Feature extraction Optics Distributed databases Adversarial machine learning Technological innovation Sensors Remote sensing Cross-domain scene classification incomplete source multisource domain adaptation unknown categories |
DOI | 10.1109/TGRS.2020.3034344 |
收录类别 | SCI ; EI |
语种 | 英语 |
WOS记录号 | WOS:000722170500022 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
EI入藏号 | 20214911290233 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.opt.ac.cn/handle/181661/95568 |
专题 | 光谱成像技术研究室 |
通讯作者 | Zheng, Xiangtao |
作者单位 | 1.Chinese Acad Sci, Xian Inst Opt & Precis Mech, Key Lab Spectral Imaging Technol CAS, Xian 710119, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China |
推荐引用方式 GB/T 7714 | Gong, Tengfei,Zheng, Xiangtao,Lu, Xiaoqiang. Cross-Domain Scene Classification by Integrating Multiple Incomplete Sources[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,2021,59(12):10035-10046. |
APA | Gong, Tengfei,Zheng, Xiangtao,&Lu, Xiaoqiang.(2021).Cross-Domain Scene Classification by Integrating Multiple Incomplete Sources.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,59(12),10035-10046. |
MLA | Gong, Tengfei,et al."Cross-Domain Scene Classification by Integrating Multiple Incomplete Sources".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 59.12(2021):10035-10046. |
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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 | ||
Cross-Domain Scene C(4091KB) | 期刊论文 | 出版稿 | 限制开放 | CC BY-NC-SA | 请求全文 |
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