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7 February 2019 Hyperspectral image secure retrieval based on encrypted deep spectral–spatial features
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Abstract
With the rapid development of remote-sensing earth observation technology, hyperspectral imagery has shown exponential growth. The quick and accurate retrieval of hyperspectral images has become a practical challenge in applications. Moreover, open network sharing has rendered network information security increasingly important. It is necessary to prevent breach of confidentiality events during retrieval, particularly for hyperspectral images containing crucial information. Therefore, a method for hyperspectral image secure retrieval based on encrypted deep spectral–spatial features is proposed. In principle, our method includes the following steps: (1) Considering the powerful feature learning capability of deep networks, deep spectral–spatial features of hyperspectral image are extracted with a deep convolutional generative adversarial network. (2) For high-dimensional deep features, t-distributed Stochastic neighbor embedding based nonlinear manifold hashing is utilized to reduce the dimensionality of deep spectral–spatial features. (3) To ensure data security during retrieval, deep spectral–spatial features are encrypted with feature randomization encryption. (4) Multi-index hashing is utilized to measure similarities among the deep spatial–spectral features of hyperspectral images. (5) Relevance feedback based on feature reweighting is introduced to further improve retrieval accuracy. Four experiments are conducted to prove the effectiveness of the proposed method based on retrieval and security performance. Our experimental results on two hyperspectral datasets show that our method can effectively protect the security of image content with sufficient image retrieval accuracy.
© 2019 Society of Photo-Optical Instrumentation Engineers (SPIE) 1931-3195/2019/$25.00 © 2019 SPIE
Jing Zhang, Lu Chen, Xi Liang, Li Zhuo, and Qi Tian "Hyperspectral image secure retrieval based on encrypted deep spectral–spatial features," Journal of Applied Remote Sensing 13(1), 018501 (7 February 2019). https://doi.org/10.1117/1.JRS.13.018501
Received: 16 September 2018; Accepted: 22 January 2019; Published: 7 February 2019
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