Please use this identifier to cite or link to this item: http://dx.doi.org/10.14279/depositonce-9351
Main Title: Advanced Local Binary Patterns for Remote Sensing Image Retrieval
Author(s): Tekeste, Issayas
Demir, Begüm
Type: Conference Object
Language Code: en
Abstract: The standard Local Binary Pattern (LBP) is considered among the most computationally efficient remote sensing (RS) image descriptors in the framework of large-scale content based RS image retrieval (CBIR). However, it has limited discrimination capability for characterizing high dimensional RS images with complex semantic content. There are several LBP variants introduced in computer vision that can be extended to RS CBIR to efficiently overcome the above-mentioned problem. To this end, this paper presents a comparative study in order to analyze and compare advanced LBP variants in RS CBIR domain. We initially introduce a categorization of the LBP variants based on the specific CBIR problems in RS, and analyze the most recent methodological developments associated to each category. All the considered LBP variants are introduced for the first time in the framework of RS image retrieval problems, and have been experimentally compared in terms of their: 1) discrimination capability to model high-level semantic information present in RS images (and thus the retrieval performance); and 2) computational complexities associated to retrieval and feature extraction time.
URI: https://depositonce.tu-berlin.de/handle/11303/10401
http://dx.doi.org/10.14279/depositonce-9351
Issue Date: 5-Nov-2018
Date Available: 26-Nov-2019
DDC Class: 006 Spezielle Computerverfahren
Subject(s): local binary pattern
content based image retrieval
remote sensing
Sponsor/Funder: EC/H2020/759764/EU/Accurate and Scalable Processing of Big Data in Earth Observation Fact Sheet/BigEarth
License: http://rightsstatements.org/vocab/InC/1.0/
Proceedings Title: IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Publisher Place: New York, NY
Publisher DOI: 10.1109/IGARSS.2018.8518856
Page Start: 6855
Page End: 6858
EISSN: 2153-7003
ISBN: 978-1-5386-7150-4
978-1-5386-7151-1
ISSN: 2153-6996
Notes: © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Appears in Collections:FG Remote Sensing Image Analysis Group » Publications

Files in This Item:
File Description SizeFormat 
tekeste_demir_2018.pdfAccepted manuscript536.69 kBAdobe PDFThumbnail
View/Open


Items in DepositOnce are protected by copyright, with all rights reserved, unless otherwise indicated.