Partial randomness hashing applied to remote sensing object classification

Ting Kang; Yazhou Liu; Quansen Sun

doi:10.1117/1.JRS.12.035020

25 September 2018 Partial randomness hashing applied to remote sensing object classification

Ting Kang, Yazhou Liu, Quansen Sun

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 12, Issue 3, 035020 (September 2018). https://doi.org/10.1117/1.JRS.12.035020

Abstract

Recently, object classification of remote sensing images has attracted more and more research interests due to the development of satellite and aerial vehicle technologies. Hashing learning is an efficient method to handle the huge amount of the remote sensing data. We proposed a hashing learning method named partial randomness supervised discrete hashing (PRSDH), which combines data-dependent methods and data-independent methods. It jointly learns a discrete binary codes generation and partial random constraint optimization model. By random projection, the computation complexity is reduced effectively. With the weight matrix derived from the training data, the semantic similarity between the data can be well preserved while generating the hashing codes. For the discrete constraint problem, this paper adopts the discrete cyclic coordinate descent algorithm to optimize the codes bit by bit. The experimental results show that PRSDH outperforms other comparative methods and demonstrate that PRSDH has good adaptability to the characteristic of remote sensing object.

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Ting Kang, Yazhou Liu, and Quansen Sun "Partial randomness hashing applied to remote sensing object classification," Journal of Applied Remote Sensing 12(3), 035020 (25 September 2018). https://doi.org/10.1117/1.JRS.12.035020

Received: 28 April 2018; Accepted: 5 September 2018; Published: 25 September 2018

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KEYWORDS

Remote sensing

Binary data

Sun

Image classification

Data modeling

Principal component analysis

Associative arrays

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