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Remote Sens. 2018, 10(5), 709;

Online Hashing for Scalable Remote Sensing Image Retrieval

College of Information and Control Engineering, China University of Petroleum (East China), Qingdao 266580, China
State Key Laboratory of Mathematical Engineering and Advanced Computing, Wuxi 214125, China
Institute of Information Engineering, Chinese Academy of Sciences, Beijing 100093, China
College of Computer and Information Engineering, Beijing Technology and Business University, Beijing 100048, China
Author to whom correspondence should be addressed.
Received: 9 April 2018 / Revised: 27 April 2018 / Accepted: 3 May 2018 / Published: 4 May 2018
(This article belongs to the Collection Learning to Understand Remote Sensing Images)
PDF [2195 KB, uploaded 24 May 2018]


Recently, hashing-based large-scale remote sensing (RS) image retrieval has attracted much attention. Many new hashing algorithms have been developed and successfully applied to fast RS image retrieval tasks. However, there exists an important problem rarely addressed in the research literature of RS image hashing. The RS images are practically produced in a streaming manner in many real-world applications, which means the data distribution keeps changing over time. Most existing RS image hashing methods are batch-based models whose hash functions are learned once for all and kept fixed all the time. Therefore, the pre-trained hash functions might not fit the ever-growing new RS images. Moreover, the batch-based models have to load all the training images into memory for model learning, which consumes many computing and memory resources. To address the above deficiencies, we propose a new online hashing method, which learns and adapts its hashing functions with respect to the newly incoming RS images in terms of a novel online partial random learning scheme. Our hash model is updated in a sequential mode such that the representative power of the learned binary codes for RS images are improved accordingly. Moreover, benefiting from the online learning strategy, our proposed hashing approach is quite suitable for scalable real-world remote sensing image retrieval. Extensive experiments on two large-scale RS image databases under online setting demonstrated the efficacy and effectiveness of the proposed method. View Full-Text
Keywords: hashing; remote sensing image retrieval; online learning hashing; remote sensing image retrieval; online learning

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Li, P.; Zhang, X.; Zhu, X.; Ren, P. Online Hashing for Scalable Remote Sensing Image Retrieval. Remote Sens. 2018, 10, 709.

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