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Open AccessArticle

Large-Scale Remote Sensing Image Retrieval Based on Semi-Supervised Adversarial Hashing

1
Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, International Research Center for Intelligent Perception and Computation, Joint International Research Laboratory of Intelligent Perception and Computation, School of Artificial Intelligence, Xidian University, Xi’an 710071, China
2
School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(17), 2055; https://doi.org/10.3390/rs11172055
Received: 4 July 2019 / Revised: 11 August 2019 / Accepted: 29 August 2019 / Published: 1 September 2019
(This article belongs to the Special Issue Content-Based Remote Sensing Image Retrieval)
Remote sensing image retrieval (RSIR), a superior content organization technique, plays an important role in the remote sensing (RS) community. With the number of RS images increases explosively, not only the retrieval precision but also the retrieval efficiency is emphasized in the large-scale RSIR scenario. Therefore, the approximate nearest neighborhood (ANN) search attracts the researchers’ attention increasingly. In this paper, we propose a new hash learning method, named semi-supervised deep adversarial hashing (SDAH), to accomplish the ANN for the large-scale RSIR task. The assumption of our model is that the RS images have been represented by the proper visual features. First, a residual auto-encoder (RAE) is developed to generate the class variable and hash code. Second, two multi-layer networks are constructed to regularize the obtained latent vectors using the prior distribution. These two modules mentioned are integrated under the generator adversarial framework. Through the minimax learning, the class variable would be a one-hot-like vector while the hash code would be the binary-like vector. Finally, a specific hashing function is formulated to enhance the quality of the generated hash code. The effectiveness of the hash codes learned by our SDAH model was proved by the positive experimental results counted on three public RS image archives. Compared with the existing hash learning methods, the proposed method reaches improved performance. View Full-Text
Keywords: hash learning; large-scale remote sensing image retrieval hash learning; large-scale remote sensing image retrieval
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MDPI and ACS Style

Tang, X.; Liu, C.; Ma, J.; Zhang, X.; Liu, F.; Jiao, L. Large-Scale Remote Sensing Image Retrieval Based on Semi-Supervised Adversarial Hashing. Remote Sens. 2019, 11, 2055.

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