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Open AccessArticle

Low-Rank Hypergraph Hashing for Large-Scale Remote Sensing Image Retrieval

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School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
2
Guangdong Provincial Key Laboratory of Petrochemical Equipment Fault Diagnosis, Guangdong University of Petrochemical Technology, Maoming 525000, China
3
Department of Communications, Polytechnic University of Valencia, 46022 Camino de Vera, Valencia, Spain
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(7), 1164; https://doi.org/10.3390/rs12071164
Received: 11 February 2020 / Revised: 2 April 2020 / Accepted: 2 April 2020 / Published: 4 April 2020
(This article belongs to the Special Issue New Advances on Sub-pixel Processing: Unmixing and Mapping Methods)
As remote sensing (RS) images increase dramatically, the demand for remote sensing image retrieval (RSIR) is growing, and has received more and more attention. The characteristics of RS images, e.g., large volume, diversity and high complexity, make RSIR more challenging in terms of speed and accuracy. To reduce the retrieval complexity of RSIR, a hashing technique has been widely used for RSIR, mapping high-dimensional data into a low-dimensional Hamming space while preserving the similarity structure of data. In order to improve hashing performance, we propose a new hash learning method, named low-rank hypergraph hashing (LHH), to accomplish for the large-scale RSIR task. First, LHH employs a l2-1 norm to constrain the projection matrix to reduce the noise and redundancy among features. In addition, low-rankness is also imposed on the projection matrix to exploit its global structure. Second, LHH uses hypergraphs to capture the high-order relationship among data, and is very suitable to explore the complex structure of RS images. Finally, an iterative algorithm is developed to generate high-quality hash codes and efficiently solve the proposed optimization problem with a theoretical convergence guarantee. Extensive experiments are conducted on three RS image datasets and one natural image dataset that are publicly available. The experimental results demonstrate that the proposed LHH outperforms the existing hashing learning in RSIR tasks. View Full-Text
Keywords: hashing; low-rank; hypergraph; remote sensing image retrieval hashing; low-rank; hypergraph; remote sensing image retrieval
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Kong, J.; Sun, Q.; Mukherjee, M.; Lloret, J. Low-Rank Hypergraph Hashing for Large-Scale Remote Sensing Image Retrieval. Remote Sens. 2020, 12, 1164.

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