Deep Cross-Modal ImageVoice Retrieval in Remote Sensing
文献类型:期刊论文
作者 | Chen, Yaxiong1,2; Lu, Xiaoqiang2; Wang, Shuai2 |
刊名 | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING |
出版日期 | 2020-10 |
卷号 | 58期号:10页码:7049-7061 |
ISSN号 | 0196-2892;1558-0644 |
关键词 | Remote sensing Convolution Quantization (signal) Image retrieval Deep learning Convolutional codes Hamming distance Cross-modal remote sensing image-voice retrieval deep features' similarity deep hash codes quantization error |
DOI | 10.1109/TGRS.2020.2979273 |
产权排序 | 1 |
英文摘要 | With the rapid progress of satellite and aircraft technologies, cross-modal remote sensing imagevoice retrieval has been studied in geography recently. However, there still exist some bottlenecks: how to consider the characteristics of remote sensing data adequately and how to reduce the memory and improve the retrieval efficiency in large-scale remote sensing data. In this article, we propose a novel deep cross-modal remote sensing imagevoice retrieval approach, namely, deep imagevoice retrieval (DIVR), to capture more information of remote sensing data to generate hash codes with low memory and fast retrieval properties. Especially, the DIVR approach proposes inception dilated convolution module to capture multiscale contextual information of remote sensing images and voices. Moreover, in order to enhance cross-modal similarity, the deep features similarity term is designed to make paired similar deep features as close as possible and paired dissimilar deep features as mutually far as possible. In addition, the quantization error term is designed to drive hash-like codes to approximate hash codes, which can effectively reduce the quantization error for hash codes learning. Extensive experimental results on three remote sensing imagevoice data sets show that the proposed DIVR approach can outperform other cross-modal retrieval approaches. |
语种 | 英语 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
WOS记录号 | WOS:000573923100021 |
源URL | [http://ir.opt.ac.cn/handle/181661/93727] |
专题 | 西安光学精密机械研究所_光学影像学习与分析中心 |
通讯作者 | Lu, Xiaoqiang |
作者单位 | 1.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 2.Chinese Acad Sci, Xian Inst Opt & Precis Mech, Key Lab Spectral Imaging Technol CAS, Xian 710119, Peoples R China |
推荐引用方式 GB/T 7714 | Chen, Yaxiong,Lu, Xiaoqiang,Wang, Shuai. Deep Cross-Modal ImageVoice Retrieval in Remote Sensing[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,2020,58(10):7049-7061. |
APA | Chen, Yaxiong,Lu, Xiaoqiang,&Wang, Shuai.(2020).Deep Cross-Modal ImageVoice Retrieval in Remote Sensing.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,58(10),7049-7061. |
MLA | Chen, Yaxiong,et al."Deep Cross-Modal ImageVoice Retrieval in Remote Sensing".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 58.10(2020):7049-7061. |
入库方式: OAI收割
来源:西安光学精密机械研究所
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