Please use this identifier to cite or link to this item: http://dx.doi.org/10.14279/depositonce-9327
For citation please use:
Main Title: An Unsupervised Multicode Hashing Method for Accurate and Scalable Remote Sensing Image Retrieval
Author(s): Reato, Thomas
Demir, Begüm
Bruzzone, Lorenzo
Type: Article
Language Code: en
Abstract: Hashing methods have recently attracted great attention for approximate nearest neighbor search in massive remote sensing (RS) image archives due to their computational and storage effectiveness. The existing hashing methods in RS represent each image with a single-hash code that is usually obtained by applying hash functions to global image representations. Such an approach may not optimally represent the complex information content of RS images. To overcome this problem, in this letter, we present a simple yet effective unsupervised method that represents each image with primitive-cluster sensitive multi-hash codes (each of which corresponds to a primitive present in the image). To this end, the proposed method consists of two main steps: 1) characterization of images by descriptors of primitive-sensitive clusters and 2) definition of multi-hash codes from the descriptors of the primitive-sensitive clusters. After obtaining multi-hash codes for each image, retrieval of images is achieved based on a multi-hash-code-matching scheme. Any hashing method that provides single-hash code can be embedded within the proposed method to provide primitive-sensitive multi-hash codes. Compared with state-of-the-art single-code hashing methods in RS, the proposed method achieves higher retrieval accuracy under the same retrieval time, and thus it is more efficient for operational applications.
URI: https://depositonce.tu-berlin.de/handle/11303/10367
http://dx.doi.org/10.14279/depositonce-9327
Issue Date: 2019
Date Available: 21-Nov-2019
DDC Class: 005 Computerprogrammierung, Programme, Daten
Subject(s): image retrieval
content-based image retrieval
image information mining
multicode hashing
remote sensing
RS
big data
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 Geoscience and Remote Sensing Letters
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Publisher Place: New York, NY
Volume: 16
Issue: 2
Publisher DOI: 10.1109/LGRS.2018.2870686
Page Start: 276
Page End: 280
EISSN: 1558-0571
ISSN: 1545-598X
Notes: © 2019 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 
reato_etal_2019.pdfAccepted manuscript680 kBAdobe PDFView/Open

Item Export Bar

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