Visual Localization Based on Remote Sensing Scene Matching with Siamese Feature Aggregation Network

Wang Chen, Yuan Yuan, Ganchao Liu

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

6 Scopus citations

Abstract

This paper presents a new framework with a siamese feature aggregation network (SFANet) for visual localization based on remote sensing scene matching. Specifically, the presented framework predicts the location of a query image by finding the matching remote sensing images with geographical information. We employ the fully convolutional networks (FCNs) and a siamese network of NetVLAD to aggregate local features and learn the global representations for images from different sources. A new soft margin loss function is established for the network. Geographic coordinates of the query images are obtained by calculating the similarity with satellite images. We also collect a multi-scale dataset that contains 136959 images from 45653 locations. Various experiments are carried out on it. Experimental results show the effectivity of the proposed method.

Original languageEnglish
Title of host publication2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages6738-6741
Number of pages4
ISBN (Electronic)9781728163741
DOIs
StatePublished - 26 Sep 2020
Event2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Virtual, Waikoloa, United States
Duration: 26 Sep 20202 Oct 2020

Publication series

NameInternational Geoscience and Remote Sensing Symposium (IGARSS)

Conference

Conference2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020
Country/TerritoryUnited States
CityVirtual, Waikoloa
Period26/09/202/10/20

Keywords

  • geolocalization
  • image retrieval
  • scene matching
  • siamese network
  • Visual localization

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