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Open AccessArticle

A Discriminative Feature Learning Approach for Remote Sensing Image Retrieval

Research Institute of information Fusion, Naval Aviation University, Yantai 264001, China
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Remote Sens. 2019, 11(3), 281; https://doi.org/10.3390/rs11030281
Received: 30 December 2018 / Revised: 23 January 2019 / Accepted: 27 January 2019 / Published: 1 February 2019
(This article belongs to the Special Issue Image Retrieval in Remote Sensing)
Effective feature representations play a decisive role in content-based remote sensing image retrieval (CBRSIR). Recently, learning-based features have been widely used in CBRSIR and they show powerful ability of feature representations. In addition, a significant effort has been made to improve learning-based features from the perspective of the network structure. However, these learning-based features are not sufficiently discriminative for CBRSIR. In this paper, we propose two effective schemes for generating discriminative features for CBRSIR. In the first scheme, the attention mechanism and a new attention module are introduced to the Convolutional Neural Networks (CNNs) structure, causing more attention towards salient features, and the suppression of other features. In the second scheme, a multi-task learning network structure is proposed, to force learning-based features to be more discriminative, with inter-class dispersion and intra-class compaction, through penalizing the distances between the feature representations and their corresponding class centers. Then, a new method for constructing more challenging datasets is first used for remote sensing image retrieval, to better validate our schemes. Extensive experiments on challenging datasets are conducted to evaluate the effectiveness of our two schemes, and the comparison of the results demonstrate that our proposed schemes, especially the fusion of the two schemes, can improve the baseline methods by a significant margin. View Full-Text
Keywords: attention mechanism; discriminative feature learning; center loss; remote sensing image retrieval attention mechanism; discriminative feature learning; center loss; remote sensing image retrieval
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MDPI and ACS Style

Xiong, W.; Lv, Y.; Cui, Y.; Zhang, X.; Gu, X. A Discriminative Feature Learning Approach for Remote Sensing Image Retrieval. Remote Sens. 2019, 11, 281. https://doi.org/10.3390/rs11030281

AMA Style

Xiong W, Lv Y, Cui Y, Zhang X, Gu X. A Discriminative Feature Learning Approach for Remote Sensing Image Retrieval. Remote Sensing. 2019; 11(3):281. https://doi.org/10.3390/rs11030281

Chicago/Turabian Style

Xiong, Wei; Lv, Yafei; Cui, Yaqi; Zhang, Xiaohan; Gu, Xiangqi. 2019. "A Discriminative Feature Learning Approach for Remote Sensing Image Retrieval" Remote Sens. 11, no. 3: 281. https://doi.org/10.3390/rs11030281

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