Paper Info
Title
Remote Sensing Cross-Modal Text-Image Retrieval Based on Global and Local Information
Abstract
Cross-modal remote sensing text-image retrieval (RSCTIR) has recently become an urgent research hotspot due to its ability of enabling fast and flexible information extraction on remote sensing (RS) images. However, current RSCTIR methods mainly focus on global features of RS images, which leads to the neglect of local features that reflect target relationships and saliency. In this article, we first propose a novel RSCTIR framework based on global and local information (GaLR), and design a multi-level information dynamic fusion (MIDF) module to efficaciously integrate features of different levels. MIDF leverages local information to correct global information, utilizes global information to supplement local information, and uses the dynamic addition of the two to generate prominent visual representation. To alleviate the pressure of the redundant targets on the graph convolution network (GCN) and to improve the model's attention on salient instances during modeling local features, the denoised representation matrix and the enhanced adjacency matrix (DREA) are devised to assist GCN in producing superior local representations. UREA not only filters out redundant features with high similarity, but also obtains more powerful local features by enhancing the features of prominent objects. Finally, to make full use of the information in the similarity matrix during inference, we come up with a plug-and-play multivariate rerank (MR) algorithm. The algorithm utilizes the k nearest neighbors of the retrieval results to perform a reverse search, and improves the performance by combining multiple components of bidirectional retrieval. Extensive experiments on public datasets strongly demonstrate the state-of-the-art performance of GaLR methods on the RSCTIR task. The code of GaLR method, MR algorithm, and corresponding files have been made available at: https://github.com/xiaoyuan1996/GaLR.
Year
DOI
Venue
2022
10.1109/TGRS.2022.3163706
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
Keywords
DocType
Volume
Cross-modal remote sensing text-image retrieval (RSCTIR), local feature optimization, multi-level information dynamic fusion (MIDF), multivariate rerank (MR)
Journal
60
ISSN
Citations 
PageRank 
0196-2892
0
0.34
References 
Authors
0
8
Name
Order
Citations
PageRank
Zhiqiang Yuan101.69
Wenkai Zhang204.73
Changyuan Tian300.34
Xuee Rong401.01
Zhengyuan Zhang500.68
Hongqi Wang601.69
Kun Fu741457.81
Xian Sun808.45