Please use this identifier to cite or link to this item: http://dx.doi.org/10.14279/depositonce-10872
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Main Title: Unsupervised Remote Sensing Image Retrieval Using Probabilistic Latent Semantic Hashing
Author(s): Fernandez-Beltran, Ruben
Demir, Begüm
Pla, Filiberto
Plaza, Antonio
Type: Article
Language Code: en
Abstract: Unsupervised hashing methods have attracted considerable attention in large-scale remote sensing (RS) image retrieval, due to their capability for massive data processing with significantly reduced storage and computation. Although existing unsupervised hashing methods are suitable for operational applications, they exhibit limitations when accurately modeling the complex semantic content present in RS images using binary codes (in an unsupervised manner). To address this problem, in this letter, we introduce a novel unsupervised hashing method that takes advantage of the generative nature of probabilistic topic models to encapsulate the hidden semantic patterns of the data into the final binary representation. Specifically, we introduce a new probabilistic latent semantic hashing (pLSH) model to effectively learn the hash codes using three main steps: 1) data grouping, where the input RS archive is clustered into several groups; 2) topic computation, where the pLSH model is used to uncover highly descriptive hidden patterns from each group; and 3) hash code generation, where the data probability distributions are thresholded to generate the final binary codes. Our experimental results, obtained on two benchmark archives, reveal that the proposed method significantly outperforms state-of-the-art unsupervised hashing methods.
URI: https://depositonce.tu-berlin.de/handle/11303/11992
http://dx.doi.org/10.14279/depositonce-10872
Issue Date: 6-Feb-2020
Date Available: 18-Nov-2020
DDC Class: 006 Spezielle Computerverfahren
Subject(s): hash codes
image retrieval
probabilistic topic models
remote sensing
RS
unsupervised hashing
Sponsor/Funder: EC/H2020/734541/EU/Tools for Mapping Human Exposure to Risky Environmental conditions by means of Ground and Earth Observation Data/EOXPOSURE
EC/H2020/759764/EU/Accurate and Scalable Processing of Big Data in Earth Observation/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
Publisher DOI: 10.1109/LGRS.2020.2969491
EISSN: 1558-0571
ISSN: 1545-598X
Appears in Collections:FG Remote Sensing Image Analysis Group » Publications

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