Multi-branch convolutional descriptors for content-based remote sensing image retrieval

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Abstract

Context-based remote sensing image retrieval (CBRSIR) is an important problem in computer vision with many applications such as military, agriculture, and surveillance. In this study, inspired by recent developments in person re-identification, we design and fine-tune a multi-branch deep learning architecture that combines global and local features to obtain rich and discriminative image representations. Additionally, we propose a new evaluation strategy that fully separates the test and training sets and where new unseen data is used for querying, thereby emphasizing the generalization capability of retrieval systems. Extensive evaluations show that our method significantly outperforms the existing approaches by up to 10.7% in mean precision@20 on popular CBRSIR datasets. Regarding the new evaluation strategy, our method attains excellent retrieval performance, yielding more than 95% precision@20 score on the challenging PatternNet dataset.

Original languageEnglish
Title of host publicationVISIGRAPP 2020 - Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications
EditorsGiovanni Maria Farinella, Petia Radeva, Jose Braz
PublisherSCITEPRESS-Science and Technology Publications, Lda.
Pages242-249
Number of pages8
Volume5
ISBN (Electronic)9789897584022
Publication statusPublished - 1 Jan 2020
Event15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISIGRAPP 2020 - Valletta, Malta
Duration: 27 Feb 202029 Feb 2020

Conference

Conference15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISIGRAPP 2020
CountryMalta
CityValletta
Period27/02/2029/02/20

Keywords

  • Content-based Image Retrieval
  • Convolutional Neural Networks
  • Local Feature Extraction
  • Remote Sensing

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