elib
DLR-Header
DLR-Logo -> http://www.dlr.de
DLR Portal Home | Imprint | Privacy Policy | Contact | Deutsch
Fontsize: [-] Text [+]

Exploiting Deep Features for Remote Sensing Image Retrieval: A Systematic Investigation

Tong, Xin-Yi and Xia, Gui-Song and Hu, Fan and Zhong, Yanfei and Datcu, Mihai and Zhang, Liangpei (2020) Exploiting Deep Features for Remote Sensing Image Retrieval: A Systematic Investigation. IEEE Transactions on Big Data. IEEE - Institute of Electrical and Electronics Engineers. DOI: 10.1109/TBDATA.2019.2948924 ISSN 2332-7790

Full text not available from this repository.

Official URL: https://ieeexplore.ieee.org/document/8880494

Abstract

Remote sensing (RS) image retrieval is of great significant for geological information mining. Over the past two decades, a large amount of research on this task has been carried out, which mainly focuses on the following three core issues: feature extraction, similarity metric and relevance feedback. Due to the complexity and multiformity of ground objects in high-resolution remote sensing (HRRS) images, there is still room for improvement in the current retrieval approaches. In this paper, we analyze the three core issues of RS image retrieval and provide a comprehensive review on existing methods. Furthermore, for the goal to advance the state-of-the-art in HRRS image retrieval, we focus on the feature extraction issue and delve how to use powerful deep representations to address this task. We conduct systematic investigation on evaluating correlative factors that may affect the performance of deep features. By optimizing each factor, we acquire remarkable retrieval results on publicly available HRRS datasets. Finally, we explain the experimental phenomenon in detail and draw conclusions according to our analysis. Our work can serve as a guiding role for the research of content-based RS image retrieval.

Item URL in elib:https://elib.dlr.de/130514/
Document Type:Article
Title:Exploiting Deep Features for Remote Sensing Image Retrieval: A Systematic Investigation
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Tong, Xin-YiState Key Lab. LIESMARS, Wuhan University, Wuhan 430079, ChinaUNSPECIFIED
Xia, Gui-SongState Key Lab. LIESMARS, Wuhan University, Wuhan 430079, ChinaUNSPECIFIED
Hu, FanSchool of Electronic Information, Wuhan University, Wuhan 430079, ChinaUNSPECIFIED
Zhong, YanfeiState Key Lab. LIESMARS, Wuhan University, Wuhan 430079, ChinaUNSPECIFIED
Datcu, MihaiMihai.Datcu (at) dlr.deUNSPECIFIED
Zhang, LiangpeiState Key Lab. LIESMARS, Wuhan University, Wuhan 430079, ChinaUNSPECIFIED
Date:2020
Journal or Publication Title:IEEE Transactions on Big Data
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:Yes
DOI :10.1109/TBDATA.2019.2948924
Publisher:IEEE - Institute of Electrical and Electronics Engineers
ISSN:2332-7790
Status:Accepted
Keywords:Remote Sensing, Image Retrieval, Deep Features
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Erdbeobachtung
DLR - Research theme (Project):R - Vorhaben hochauflösende Fernerkundungsverfahren
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > EO Data Science
Deposited By: Karmakar, Chandrabali
Deposited On:03 Dec 2019 12:16
Last Modified:14 Dec 2019 04:25

Repository Staff Only: item control page

Browse
Search
Help & Contact
Information
electronic library is running on EPrints 3.3.12
Copyright © 2008-2017 German Aerospace Center (DLR). All rights reserved.