Editorial for the Special Issue "Frontiers in Spectral Imaging and 3D Technologies for Geospatial Solutions"

Honkavaara, Eija; Karantzalos, Konstantinos; Liang, Xinlian; Nocerino, Erica; Pölönen, Ilkka; Rönnholm, Petri

Aaltodoc
→
6b Artikkelit / Articles from Research information portal
→
[article-cris] Insinööritieteiden korkeakoulu / ENG
→
View Item

Editorial for the Special Issue "Frontiers in Spectral Imaging and 3D Technologies for Geospatial Solutions"

Altmetrics



Title:	Editorial for the Special Issue "Frontiers in Spectral Imaging and 3D Technologies for Geospatial Solutions"
Author(s):	Honkavaara, Eija ; Karantzalos, Konstantinos ; Liang, Xinlian ; Nocerino, Erica ; Pölönen, Ilkka ; Rönnholm, Petri
Date:	2019-07-02
Language:	en
Pages:	3
Department:	National Land Survey of Finland National Technical University of Athens Aix-Marseille Université University of Jyväskylä Geoinformatics Department of Built Environment
Series:	Remote Sensing, Volume 11, issue 14
ISSN:	2072-4292
DOI-number:	10.3390/rs11141714
Keywords:	hyperspectral imaging, point cloud, sensor integration, data fusion, machine learning, deep learning, classification, estimation, semantic segmentation, object detection, point cloud filtering
» Show full item record
This publication is imported from Aalto University Research information portal: https://research.aalto.fi > View this publication in Research information portal > View/Open full text file from Research information portal
Citation: Honkavaara , E , Karantzalos , K , Liang , X , Nocerino , E , Pölönen , I & Rönnholm , P 2019 , ' Editorial for the Special Issue "Frontiers in Spectral Imaging and 3D Technologies for Geospatial Solutions" ' , Remote Sensing , vol. 11 , no. 14 , 1714 . https://doi.org/10.3390/rs11141714
Abstract: This Special Issue hosts papers on the integrated use of spectral imaging and 3D technologies in remote sensing, including novel sensors, evolving machine learning technologies for data analysis, and the utilization of these technologies in a variety of geospatial applications. The presented results showed improved results when multimodal data was used in object analysis.

Permanent link to this item: http://urn.fi/URN:NBN:fi:aalto-202002212252 Email this Export to RefWorks QR Code Print BibTex Tweet