Multi-level Feedback Joint Representation Learning Network Based on Adaptive Area Elimination for Cross-view Geo-localization

Fawei Ge, Yunzhou Zhang, Li Wang, Wei Liu, Yixiu Liu, Sonya Coleman, Dermot Kerr

Research output: Contribution to journalArticlepeer-review

12 Downloads (Pure)

Abstract

Cross-view geo-localization refers to the task of matching the same geographic target using images obtained from different platforms, such as drone-view and satellite-view. However, the view angle of images obtained through different platforms will vary greatly, which can bring great challenges to the cross-view geo-localization task. Therefore, we propose a multi-level feedback joint representation learning network based on adaptive area elimination to solve the cross-view geo-localization problem. In our network model, we first process the extracted global features to obtain part-level and patch-level features. We then utilize these features as feedback to the global features to extract the contextual information in the global features and improve the robustness of the extracted features. In addition, as images obtained from different platforms differ, there will always be some interference when matching images. Therefore, we introduce an adaptive area elimination strategy to erase the interference information in the global features and assist the model in obtaining crucial information. On this basis, the feature correlation loss function is designed to constrain learning when using global feature information, thereby eliminating the possible interference, which can improve the network model performance. Finally, a series of experiments is carried out using two well-known benchmarks, namely University-1652 and SUES-200, and the experimental results show that the proposed network model achieves competitive results, thereby demonstrating the effectiveness of proposed model.

Original languageEnglish
Article number5913915
Pages (from-to)1-15
Number of pages15
JournalIEEE Transactions on Geoscience and Remote Sensing
Volume62
Early online date2 May 2024
DOIs
Publication statusPublished online - 2 May 2024

Bibliographical note

Publisher Copyright:
© 1980-2012 IEEE.

Keywords

  • Adaptive area elimination
  • geo-localization
  • interference information
  • multilevel feedback

Fingerprint

Dive into the research topics of 'Multi-level Feedback Joint Representation Learning Network Based on Adaptive Area Elimination for Cross-view Geo-localization'. Together they form a unique fingerprint.

Cite this