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A novel rotation invariance hashing network for fast remote sensing image retrieval
Author(s): Chang Zou; Showhong Wan; Peiquan Jin; Xingyue Li
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Paper Abstract

With the increasing amount of high-resolution remote sensing images, large-scale remote sensing image retrieval(RSIR) becomes more and more significant and has attracted great attention. Traditional image retrieval methods generally use hand-crafted features which are not only time-consuming but also always get poor performance. Deep learning recently achieves remarkable performance due to its powerful ability to learn high-level semantic features, so researchers attempt to take advantage of features derived from Convolutional Neural Networks(CNNs) in RSIR. But remote sensing image is different from natural scene image, its background is more complicated with a lot of noise and existing deep learning method didn’t handle this well. Both the speed and the accuracy achieve unsatisfactory performance. In this paper, we propose a rotation invariant hashing network that represents an image as a binary hash code to retrieve image faster while considering the rotation invariance of the same target. The results of the experiments on some available remote sensing datasets show that our method is effective and outperforms than other features which is usually used in RSIR.

Paper Details

Date Published: 9 August 2018
PDF: 6 pages
Proc. SPIE 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018), 1080652 (9 August 2018); doi: 10.1117/12.2503185
Show Author Affiliations
Chang Zou, Univ. of Science and Technology of China (China)
Showhong Wan, Univ. of Science and Technology of China (China)
Peiquan Jin, Univ. of Science and Technology of China (China)
Xingyue Li, Univ. of Science and Technology of China (China)


Published in SPIE Proceedings Vol. 10806:
Tenth International Conference on Digital Image Processing (ICDIP 2018)
Xudong Jiang; Jenq-Neng Hwang, Editor(s)

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