On the Exploitation of Heterophily in Graph-Based Multimodal Remote Sensing Data Analysis

Catherine Taelman, Saloua Chlaily, Eduard Khachatrian, Fons van der Sommen, Andrea Marinoni

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Abstract

The field of Earth observation is dealing with increasingly large, multimodal data sets. An important processing step consists of providing these data sets with labels. However, standard label propagation algorithms cannot be applied to multimodal remote sensing data for two reasons. First, multimodal data is heterogeneous while classic label propagation algorithms assume a homogeneous network. Second, real-world data can show both homophily ('birds of a feather flock together') and heterophily ('opposites attract') during propagation, while standard algorithms only consider homophily. Both shortcomings are addressed in this work and the result is a graph-based label propagation algorithm for multimodal data that includes homophily and/or heterophily. Furthermore, the method is also able to transfer information between uni- and multimodal data. Experiments on the remote sensing data set of Houston, which contains a LiDAR and a hyperspectral image, show that our approach ties state-of-the-art methods for classification with an OA of 91.4%, while being more flexible and not constrained to a specific data set or a specific combination of modalities.

Original languageEnglish
Title of host publicationProceedings of the CIKM 2021 Workshops co-located with 30th ACM International Conference on Information and Knowledge Management (CIKM 2021)
EditorsGao Cong, Maya Ramanth
PublisherCEUR-WS.org
Number of pages7
Publication statusPublished - 2021
Event2021 International Conference on Information and Knowledge Management Workshops, CIKMW 2021 - Gold Coast, Australia
Duration: 1 Nov 20215 Nov 2021

Publication series

NameCEUR Workshop Proceedings
PublisherCEUR-WS.org
Volume3052
ISSN (Print)1613-0073

Conference

Conference2021 International Conference on Information and Knowledge Management Workshops, CIKMW 2021
Country/TerritoryAustralia
CityGold Coast
Period1/11/215/11/21

Funding

This work is funded in part by Centre for Integrated Remote Sensing and Forecasting for Arctic Operations ?CIRFA) and the Research Council of Norway ?RCN Grant no. 237906), and the Automatic Multisensor remote sensing for Sea Ice Characterization ? AMUSIC) Framsenteret ”Polhavet” flagship project 2020. This work is funded in part by Centre for Integrated Remote Sensing and Forecasting for Arctic Operations (CIRFA) and the Research Council of Norway (RCN Grant no. 237906), and the Automatic Multisensor remote sensing for Sea Ice Characterization (AMUSIC) Framsenteret?Polhavet? flagship project 2020.

FundersFunder number
Centre for Integrated Remote Sensing and Forecasting for Arctic Operations
Centre for Integrated Remote Sensing and Forecasting for Arctic Operations237906
Réseau de cancérologie Rossy
Norges Forskningsråd

    Keywords

    • Heterophily
    • Label propagation
    • Multimodal data
    • Remote sensing

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