A Discriminative Feature Learning Approach for Remote Sensing Image Retrieval release_4glhbdsc4jcjnof7edmobjixbi

by Wei Xiong, Yafei Lv, Yaqi Cui, Xiaohan Zhang, Xiangqi Gu

Published in Remote Sensing by MDPI AG.

2019   Volume 11, p281

Abstract

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.
In application/xml+jats format

Archived Files and Locations

application/pdf  5.5 MB
file_l2fhdzj4fnfajmzr32o62ojcpm
web.archive.org (webarchive)
res.mdpi.com (web)
Read Archived PDF
Preserved and Accessible
Type  article-journal
Stage   published
Date   2019-02-01
Language   en ?
Container Metadata
Open Access Publication
In DOAJ
In ISSN ROAD
In Keepers Registry
ISSN-L:  2072-4292
Work Entity
access all versions, variants, and formats of this works (eg, pre-prints)
Catalog Record
Revision: 39802b90-7ef6-4a9a-ac5a-050f7ab77fc3
API URL: JSON