dc.identifier.uri |
http://dx.doi.org/10.15488/10875 |
|
dc.identifier.uri |
https://www.repo.uni-hannover.de/handle/123456789/10957 |
|
dc.contributor.author |
Chen, Lie
|
|
dc.contributor.author |
Rottensteiner, Franz
|
|
dc.contributor.author |
Heipke, Christian
|
|
dc.contributor.editor |
Paparoditis, N.
|
|
dc.contributor.editor |
Mallet, C.
|
|
dc.contributor.editor |
Lafarge, F.
|
|
dc.contributor.editor |
Remondino, F.
|
|
dc.contributor.editor |
Toschi, I.
|
|
dc.contributor.editor |
Fuse, T.
|
|
dc.date.accessioned |
2021-05-04T12:14:03Z |
|
dc.date.available |
2021-05-04T12:14:03Z |
|
dc.date.issued |
2020 |
|
dc.identifier.citation |
Chen, L.; Rottensteiner, F.; Heipke, C.: Deep learning based feature matching and its application in image orientation. In: Paparoditis, N. et.al. (Eds.): XXIV ISPRS Congress, Commission II : edition 2020. Katlenburg-Lindau : Copernicus Publications, 2020 (ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences ; 5,2), S. 25-33. DOI: https://doi.org/10.5194/isprs-annals-V-2-2020-25-2020 |
|
dc.description.abstract |
Matching images containing large viewpoint and viewing direction changes, resulting in large perspective differences, still is a very challenging problem. Affine shape estimation, orientation assignment and feature description algorithms based on detected hand crafted features have shown to be error prone. In this paper, affine shape estimation, orientation assignment and description of local features is achieved through deep learning. Those three modules are trained based on loss functions optimizing the matching performance of input patch pairs. The trained descriptors are first evaluated on the Brown dataset (Brown et al., 2011), a standard descriptor performance benchmark. The whole pipeline is then tested on images of small blocks acquired with an aerial penta camera, to compute image orientation. The results show that learned features perform significantly better than alternatives based on hand crafted features. © 2020 Copernicus GmbH. All rights reserved. |
eng |
dc.language.iso |
eng |
|
dc.publisher |
Katlenburg-Lindau : Copernicus Publications |
|
dc.relation.ispartof |
XXIV ISPRS Congress, Commission II : edition 2020 |
|
dc.relation.ispartofseries |
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences ; 5,2 |
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dc.rights |
CC BY 4.0 Unported |
|
dc.rights.uri |
https://creativecommons.org/licenses/by/4.0/ |
|
dc.subject |
image matching |
eng |
dc.subject |
affine shape estimation |
eng |
dc.subject |
descriptor learning |
eng |
dc.subject |
feature orientation |
eng |
dc.subject |
image orientation |
eng |
dc.subject.classification |
Konferenzschrift |
ger |
dc.subject.ddc |
550 | Geowissenschaften
|
ger |
dc.title |
Deep learning based feature matching and its application in image orientation |
|
dc.type |
BookPart |
|
dc.type |
Text |
|
dc.relation.essn |
2194-9050 |
|
dc.relation.issn |
2194-9042 |
|
dc.relation.doi |
https://doi.org/10.5194/isprs-annals-V-2-2020-25-2020 |
|
dc.bibliographicCitation.issue |
2 |
|
dc.bibliographicCitation.volume |
5 |
|
dc.bibliographicCitation.firstPage |
25 |
|
dc.bibliographicCitation.lastPage |
33 |
|
dc.description.version |
publishedVersion |
|
tib.accessRights |
frei zug�nglich |
|