Aggregated deep local features for remote sensing image retrieval

Raffaele Imbriaco (Corresponding author), Clint Sebastian, Egor Bondarau, Peter de With

Research output: Contribution to journalArticleAcademicpeer-review

66 Citations (Scopus)
122 Downloads (Pure)


Remote Sensing Image Retrieval remains a challenging topic due to the special nature of Remote Sensing imagery. Such images contain various different semantic objects, which clearly complicates the retrieval task. In this paper, we present an image retrieval pipeline that uses attentive, local convolutional features and aggregates them using the Vector of Locally Aggregated Descriptors (VLAD) to produce a global descriptor. We study various system parameters such as the multiplicative and additive attention mechanisms and descriptor dimensionality. We propose a query expansion method that requires no external inputs. Experiments demonstrate that even without training, the local convolutional features and global representation outperform other systems. After system tuning, we can achieve state-of-the-art or competitive results. Furthermore, we observe that our query expansion method increases overall system performance by about 3%, using only the top-three retrieved images. Finally, we show how dimensionality reduction produces compact descriptors with increased retrieval performance and fast retrieval computation times, e.g., 50% faster than the current systems.
Original languageEnglish
Article number493
Number of pages23
JournalRemote Sensing
Issue number5
Publication statusPublished - 1 Mar 2019


  • image retrieval
  • convolutional descriptor
  • query expansion
  • descriptor aggregation
  • Convolutional Neural Networks
  • Convolutional descriptor
  • Image retrieval
  • Descriptor aggregation
  • Query expansion


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