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Remote Sensing Image Retrieval with Deep Features Encoding of Inception V4 and Largevis Dimensionality Reduction

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Abstract

Remote sensing image retrieval is an effective means to manage and share massive remote sensing image data. In this paper, a remote sensing image retrieval method has been proposed, which adopts Inception V4 as the backbone network to extract the deep features. To represent the low-level visual information of the remote sensing image, the feature maps generated from the first Reduction Block of Inception V4 through using 5 × 5 convolutional kernels are extracted and reorganized. Next, VLAD (Vector Locally Aggregated Descriptors) is exploited to encode the reorganized features to obtain a compact feature representation vector. The vector is cascaded with the features extracted from the fully connected layers to form the overall feature vector of the image. In order to avoid the problem of “Curse of Dimensionality”, Largevis dimensionality reduction method is utilized to reduce the dimensionality of the image feature vector, while improving its discriminative capability. The dimensionality reduced feature vector is utilized for image retrieval with L2 distance measurement metric. Experimental results on the datasets of RS19, UCM and RSSCN7 have demonstrated that, compared with the existing methods, the proposed method can obtain state-of-the-art retrieval performance.

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Acknowledgements

This work in this paper is supported by the Beijing Municipal Education Commission Cooperation Beijing Natural Science Foundation (KZ201810005002).

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Authors and Affiliations

  1. Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing University of Technology, 100 Ping leyuan, Chaoyang District, Beijing, 100124, People’s Republic of China

    Feng Hou, Bin Liu, Li Zhuo & Jing Zhang

  2. College of Microelectronics, Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, People’s Republic of China

    Feng Hou, Bin Liu, Li Zhuo & Jing Zhang

  3. School of Computer Science, Beihang University, Beijing, 100191, People’s Republic of China

    Zheng Zhuo

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  1. Feng Hou
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  2. Bin Liu
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  3. Li Zhuo
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  4. Zheng Zhuo
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  5. Jing Zhang
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Correspondence to Li Zhuo.

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Hou, F., Liu, B., Zhuo, L. et al. Remote Sensing Image Retrieval with Deep Features Encoding of Inception V4 and Largevis Dimensionality Reduction. Sens Imaging 22, 20 (2021). https://doi.org/10.1007/s11220-021-00341-7

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  • DOI: https://doi.org/10.1007/s11220-021-00341-7

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