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Open AccessArticle

Multiple Feature Hashing Learning for Large-Scale Remote Sensing Image Retrieval

1
School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
2
School of Geosciences and Info-Physics, Central South University, Changsha 410083, China
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2017, 6(11), 364; https://doi.org/10.3390/ijgi6110364
Received: 12 September 2017 / Revised: 6 November 2017 / Accepted: 13 November 2017 / Published: 16 November 2017
Driven by the urgent demand of remote sensing big data management and knowledge discovery, large-scale remote sensing image retrieval (LSRSIR) has attracted more and more attention. As is well known, hashing learning has played an important role in coping with big data mining problems. In the literature, several hashing learning methods have been proposed to address LSRSIR. Until now, existing LSRSIR methods take only one type of feature descriptor as the input of hashing learning methods and ignore the complementary effects of multiple features, which may represent remote sensing images from different aspects. Different from the existing LSRSIR methods, this paper proposes a flexible multiple-feature hashing learning framework for LSRSIR, which takes multiple complementary features as the input and learns the hybrid feature mapping function, which projects multiple features of the remote sensing image to the low-dimensional binary (i.e., compact) feature representation. Furthermore, the compact feature representations can be directly utilized in LSRSIR with the aid of the hamming distance metric. In order to show the superiority of the proposed multiple feature hashing learning method, we compare the proposed approach with the existing methods on two publicly available large-scale remote sensing image datasets. Extensive experiments demonstrate that the proposed approach can significantly outperform the state-of-the-art approaches. View Full-Text
Keywords: multiple feature hashing learning; large-scale remote sensing image retrieval; remote sensing big data management multiple feature hashing learning; large-scale remote sensing image retrieval; remote sensing big data management
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Ye, D.; Li, Y.; Tao, C.; Xie, X.; Wang, X. Multiple Feature Hashing Learning for Large-Scale Remote Sensing Image Retrieval. ISPRS Int. J. Geo-Inf. 2017, 6, 364.

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