Please use this identifier to cite or link to this item: http://dx.doi.org/10.14279/depositonce-9352
Main Title: Deep Metric and Hash-Code Learning for Content-Based Retrieval of Remote Sensing Images
Author(s): Roy, Subhankar
Sangineto, Enver
Demir, Begüm
Sebe, Nicu
Type: Conference Object
Language Code: en
Abstract: The growing volume of Remote Sensing (RS) image archives demands for feature learning techniques and hashing functions which can: (1) accurately represent the semantics in the RS images; and (2) have quasi real-time performance during retrieval. This paper aims to address both challenges at the same time, by learning a semantic-based metric space for content based RS image retrieval while simultaneously producing binary hash codes for an efficient archive search. This double goal is achieved by training a deep network using a combination of different loss functions which, on the one hand, aim at clustering semantically similar samples (i.e., images), and, on the other hand, encourage the network to produce final activation values (i.e., descriptors) that can be easily binarized. Moreover, since RS annotated training images are too few to train a deep network from scratch, we propose to split the image representation problem in two different phases. In the first we use a general-purpose, pre-trained network to produce an intermediate representation, and in the second we train our hashing network using a relatively small set of training images. Experiments on two aerial benchmark archives show that the proposed method outperforms previous state-of-the-art hashing approaches by up to 5.4% using the same number of hash bits per image.
URI: https://depositonce.tu-berlin.de/handle/11303/10402
http://dx.doi.org/10.14279/depositonce-9352
Issue Date: 5-Nov-2018
Date Available: 26-Nov-2019
DDC Class: 006 Spezielle Computerverfahren
Subject(s): deep hashing
metric learning
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.2018.8518381
Page Start: 4539
Page End: 4542
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

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