Multiclass Coarse Analysis for UAV Imagery

This paper presents a novel method to "coarsely" describe extremely high-resolution (EHR) images acquired by means of unmanned aerial vehicles (UAVs) over urban areas. Standard image analysis approaches cannot be directly exploited for the automatic description of UAV images due to their E...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Thomas Moranduzzo [verfasserIn] Farid Melgani Mohamed Lamine Mekhalfi Yakoub Bazi Naif Alajlan

Format:	Artikel
Sprache:	Englisch

Erschienen:	2015

Schlagwörter:	Unmanned aerial vehicles

Übergeordnetes Werk:	Enthalten in: IEEE transactions on geoscience and remote sensing - New York, NY : IEEE, 1964, 53(2015), 12, Seite 6394-13
Übergeordnetes Werk:	volume:53 ; year:2015 ; number:12 ; pages:6394-13

Links:	Volltext Link aufrufen

DOI / URN:	10.1109/TGRS.2015.2438400

Katalog-ID:	OLC1965775462

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spelling	10.1109/TGRS.2015.2438400 doi PQ20160617 (DE-627)OLC1965775462 (DE-599)GBVOLC1965775462 (PRQ)c2067-6fa0e07f925ed5905700996f7426d7bdf8aacc10cde172dfb7eda277a1d5de7b0 (KEY)0048677920150000053001206394multiclasscoarseanalysisforuavimagery DE-627 ger DE-627 rakwb eng 620 550 DNB Thomas Moranduzzo verfasserin aut Multiclass Coarse Analysis for UAV Imagery 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier This paper presents a novel method to "coarsely" describe extremely high-resolution (EHR) images acquired by means of unmanned aerial vehicles (UAVs) over urban areas. Standard image analysis approaches cannot be directly exploited for the automatic description of UAV images due to their EHR. For this reason, we propose an alternative approach that consists first in the subdivision of the original UAV image in a grid of tiles. Then, each tile is compared with a library of training tiles to inherit the binary multilabel vector of the most similar training tile. This vector conveys a list of classes likely present in the considered tile. Our multiclass tile-based approach needs the definition of two main ingredients: 1) a suitable tile-representation strategy; and 2) a tile-to-tile matching operation. Various tile-representation and matching strategies are investigated. In particular, we present three global representation strategies, which process each tile as a whole and two point-based strategies that exploit points of interest within the considered tile. Regarding the matching strategies, two simple measures of distance, namely, the Euclidean and the chi-squared histogram distances, are explored. Interesting experimental results conducted on a rich set of real UAV images acquired over an urban area are reported and discussed. Unmanned aerial vehicles Farid Melgani oth Mohamed Lamine Mekhalfi oth Yakoub Bazi oth Naif Alajlan oth Enthalten in IEEE transactions on geoscience and remote sensing New York, NY : IEEE, 1964 53(2015), 12, Seite 6394-13 (DE-627)129601667 (DE-600)241439-9 (DE-576)015095282 0196-2892 nnns volume:53 year:2015 number:12 pages:6394-13 http://dx.doi.org/10.1109/TGRS.2015.2438400 Volltext http://search.proquest.com/docview/1728007297 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-ARC SSG-OLC-TEC SSG-OLC-GEO SSG-OLC-FOR SSG-OPC-GGO SSG-OPC-GEO GBV_ILN_70 GBV_ILN_2027 AR 53 2015 12 6394-13
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title_sort	multiclass coarse analysis for uav imagery
title_auth	Multiclass Coarse Analysis for UAV Imagery
abstract	This paper presents a novel method to "coarsely" describe extremely high-resolution (EHR) images acquired by means of unmanned aerial vehicles (UAVs) over urban areas. Standard image analysis approaches cannot be directly exploited for the automatic description of UAV images due to their EHR. For this reason, we propose an alternative approach that consists first in the subdivision of the original UAV image in a grid of tiles. Then, each tile is compared with a library of training tiles to inherit the binary multilabel vector of the most similar training tile. This vector conveys a list of classes likely present in the considered tile. Our multiclass tile-based approach needs the definition of two main ingredients: 1) a suitable tile-representation strategy; and 2) a tile-to-tile matching operation. Various tile-representation and matching strategies are investigated. In particular, we present three global representation strategies, which process each tile as a whole and two point-based strategies that exploit points of interest within the considered tile. Regarding the matching strategies, two simple measures of distance, namely, the Euclidean and the chi-squared histogram distances, are explored. Interesting experimental results conducted on a rich set of real UAV images acquired over an urban area are reported and discussed.
abstractGer	This paper presents a novel method to "coarsely" describe extremely high-resolution (EHR) images acquired by means of unmanned aerial vehicles (UAVs) over urban areas. Standard image analysis approaches cannot be directly exploited for the automatic description of UAV images due to their EHR. For this reason, we propose an alternative approach that consists first in the subdivision of the original UAV image in a grid of tiles. Then, each tile is compared with a library of training tiles to inherit the binary multilabel vector of the most similar training tile. This vector conveys a list of classes likely present in the considered tile. Our multiclass tile-based approach needs the definition of two main ingredients: 1) a suitable tile-representation strategy; and 2) a tile-to-tile matching operation. Various tile-representation and matching strategies are investigated. In particular, we present three global representation strategies, which process each tile as a whole and two point-based strategies that exploit points of interest within the considered tile. Regarding the matching strategies, two simple measures of distance, namely, the Euclidean and the chi-squared histogram distances, are explored. Interesting experimental results conducted on a rich set of real UAV images acquired over an urban area are reported and discussed.
abstract_unstemmed	This paper presents a novel method to "coarsely" describe extremely high-resolution (EHR) images acquired by means of unmanned aerial vehicles (UAVs) over urban areas. Standard image analysis approaches cannot be directly exploited for the automatic description of UAV images due to their EHR. For this reason, we propose an alternative approach that consists first in the subdivision of the original UAV image in a grid of tiles. Then, each tile is compared with a library of training tiles to inherit the binary multilabel vector of the most similar training tile. This vector conveys a list of classes likely present in the considered tile. Our multiclass tile-based approach needs the definition of two main ingredients: 1) a suitable tile-representation strategy; and 2) a tile-to-tile matching operation. Various tile-representation and matching strategies are investigated. In particular, we present three global representation strategies, which process each tile as a whole and two point-based strategies that exploit points of interest within the considered tile. Regarding the matching strategies, two simple measures of distance, namely, the Euclidean and the chi-squared histogram distances, are explored. Interesting experimental results conducted on a rich set of real UAV images acquired over an urban area are reported and discussed.
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container_issue	12
title_short	Multiclass Coarse Analysis for UAV Imagery
url	http://dx.doi.org/10.1109/TGRS.2015.2438400 http://search.proquest.com/docview/1728007297
remote_bool	false
author2	Farid Melgani Mohamed Lamine Mekhalfi Yakoub Bazi Naif Alajlan
author2Str	Farid Melgani Mohamed Lamine Mekhalfi Yakoub Bazi Naif Alajlan
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doi_str	10.1109/TGRS.2015.2438400
up_date	2024-07-03T19:05:55.378Z
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