Exploiting representations from pre-trained convolutional neural networks for high-resolution remote sensing image retrieval

@article{Ge2017ExploitingRF,
  title={Exploiting representations from pre-trained convolutional neural networks for high-resolution remote sensing image retrieval},
  author={Yun Ge and Shunliang Jiang and Qingyong Xu and Changlong Jiang and Famao Ye},
  journal={Multimedia Tools and Applications},
  year={2017},
  pages={1-27}
}
With the increasing amount of high-resolution remote sensing images, it becomes more and more urgent to retrieve remote sensing images from large archives efficiently. The existing methods are mainly based on shallow features to retrieve images, while shallow features are easily affected by artificial intervention. Recently, convolutional neural networks (CNNs) are capable of learning feature representations automatically, and CNNs pre-trained on large-scale datasets are generic. This paper… CONTINUE READING

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References

Publications referenced by this paper.
SHOWING 1-10 OF 45 REFERENCES

Geographic Image Retrieval Using Local Invariant Features

  • IEEE Transactions on Geoscience and Remote Sensing
  • 2013
VIEW 13 EXCERPTS
HIGHLY INFLUENTIAL

Delving into deep representations for remote sensing image retrieval

  • 2016 IEEE 13th International Conference on Signal Processing (ICSP)
  • 2016
VIEW 10 EXCERPTS
HIGHLY INFLUENTIAL

Aggregating Local Deep Features for Image Retrieval

  • 2015 IEEE International Conference on Computer Vision (ICCV)
  • 2015
VIEW 6 EXCERPTS
HIGHLY INFLUENTIAL

A Novel Active Learning Method in Relevance Feedback for Content-Based Remote Sensing Image Retrieval

  • IEEE Transactions on Geoscience and Remote Sensing
  • 2015
VIEW 4 EXCERPTS
HIGHLY INFLUENTIAL

Remote Sensing Image Retrieval With Global Morphological Texture Descriptors

  • IEEE Transactions on Geoscience and Remote Sensing
  • 2014
VIEW 4 EXCERPTS
HIGHLY INFLUENTIAL

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