Please use this identifier to cite or link to this item: http://dx.doi.org/10.14279/depositonce-9328
Main Title: A Novel System for Content-Based Retrieval of Single and Multi-Label High-Dimensional Remote Sensing Images
Author(s): Dai, Osman Emre
Demir, Begüm
Sankur, Bülent
Bruzzone, Lorenzo
Type: Article
Language Code: en
Abstract: This paper presents a novel content-based remote sensing (RS) image retrieval system that consists of the following. First, an image description method that characterizes both spatial and spectral information content of RS images. Second, a supervised retrieval method that efficiently models and exploits the sparsity of RS image descriptors. The proposed image description method characterizes the spectral content by three different novel spectral descriptors that are: raw pixel values, simple bag of spectral values and the extended bag of spectral values descriptors. To model the spatial content of RS images, we consider the well-known scale invariant feature transform-based bag of visual words approach. With the conjunction of the spatial and the spectral descriptors, RS image retrieval is achieved by a novel sparse reconstruction-based RS image retrieval method. The proposed method considers a novel measure of label likelihood in the framework of sparse reconstruction-based classifiers and generalizes the original sparse classifier to the case both single-label and multi-label RS image retrieval problems. Finally, to enhance retrieval performance, we introduce a strategy to exploit the sensitivity of the sparse reconstruction-based method to different dictionary words. Experimental results obtained on two benchmark archives show the effectiveness of the proposed system.
URI: https://depositonce.tu-berlin.de/handle/11303/10368
http://dx.doi.org/10.14279/depositonce-9328
Issue Date: 2018
Date Available: 21-Nov-2019
DDC Class: 004 Datenverarbeitung; Informatik
Subject(s): image retrieval
multi-label image retrieval
remote sensing
sparse reconstruction-based retrieval
spatial description
spectral description
RS
SRR
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/
Journal Title: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Publisher Place: New York, NY
Volume: 11
Issue: 7
Publisher DOI: 10.1109/JSTARS.2018.2832985
Page Start: 2473
Page End: 2490
EISSN: 2151-1535
ISSN: 1939-1404
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

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