dc.identifier.uri |
http://dx.doi.org/10.15488/4067 |
|
dc.identifier.uri |
https://www.repo.uni-hannover.de/handle/123456789/4101 |
|
dc.contributor.author |
Politz, F.
|
|
dc.contributor.author |
Sester, M.
|
|
dc.contributor.editor |
Jutzi, B.
|
|
dc.contributor.editor |
Weinmann, M.
|
|
dc.contributor.editor |
Hinz, S.
|
|
dc.date.accessioned |
2018-11-30T10:09:38Z |
|
dc.date.available |
2018-11-30T10:09:38Z |
|
dc.date.issued |
2018 |
|
dc.identifier.citation |
Politz, F.; Sester, M.: Exploring ALS and DIM data for semantic segmentation using CNNs. In: Jutzi, B.; Weinmann, M.; Hinz, S. (Eds.): ISPRS TC I Mid-term Symposium "Innovative Sensing – From Sensors to Methods and Applications". Katlenburg-Lindau : Copernicus Publications, 2018 (The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ; 42-1), S. 347-354. DOI: https://doi.org/10.5194/isprs-archives-XLII-1-347-2018 |
|
dc.description.abstract |
Over the past years, the algorithms for dense image matching (DIM) to obtain point clouds from aerial images improved significantly. Consequently, DIM point clouds are now a good alternative to the established Airborne Laser Scanning (ALS) point clouds for remote sensing applications. In order to derive high-level applications such as digital terrain models or city models, each point within a point cloud must be assigned a class label. Usually, ALS and DIM are labelled with different classifiers due to their varying characteristics. In this work, we explore both point cloud types in a fully convolutional encoder-decoder network, which learns to classify ALS as well as DIM point clouds. As input, we project the point clouds onto a 2D image raster plane and calculate the minimal, average and maximal height values for each raster cell. The network then differentiates between the classes ground, non-ground, building and no data. We test our network in six training setups using only one point cloud type, both point clouds as well as several transfer-learning approaches. We quantitatively and qualitatively compare all results and discuss the advantages and disadvantages of all setups. The best network achieves an overall accuracy of 96% in an ALS and 83% in a DIM test set. |
eng |
dc.language.iso |
eng |
|
dc.publisher |
Katlenburg-Lindau : Copernicus Publications |
|
dc.relation.ispartof |
ISPRS TC I Mid-term Symposium "Innovative Sensing – From Sensors to Methods and Applications" |
|
dc.relation.ispartofseries |
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ; 42-1 |
|
dc.rights |
CC BY 4.0 Unported |
|
dc.rights.uri |
https://creativecommons.org/licenses/by/4.0/ |
|
dc.subject |
Airborne Laser Scanning |
eng |
dc.subject |
CNN |
eng |
dc.subject |
Dense Image Matching |
eng |
dc.subject |
Encoder-decoder Network |
eng |
dc.subject |
Point cloud |
eng |
dc.subject |
Semantic segmentation |
eng |
dc.subject |
Antennas |
eng |
dc.subject |
Decoding |
eng |
dc.subject |
Image enhancement |
eng |
dc.subject |
Image matching |
eng |
dc.subject |
Laser applications |
eng |
dc.subject |
Network coding |
eng |
dc.subject |
Rasterization |
eng |
dc.subject |
Remote sensing |
eng |
dc.subject |
Semantics |
eng |
dc.subject |
Airborne Laser scanning |
eng |
dc.subject |
Convolutional encoders |
eng |
dc.subject |
Digital terrain model |
eng |
dc.subject |
Encoder-decoder |
eng |
dc.subject |
High level applications |
eng |
dc.subject |
Point cloud |
eng |
dc.subject |
Remote sensing applications |
eng |
dc.subject |
Semantic segmentation |
eng |
dc.subject |
Image segmentation |
eng |
dc.subject.ddc |
550 | Geowissenschaften
|
ger |
dc.title |
Exploring ALS and DIM data for semantic segmentation using CNNs |
|
dc.type |
BookPart |
|
dc.type |
Text |
|
dc.relation.essn |
2194-9034 |
|
dc.relation.issn |
1682-1750 |
|
dc.relation.doi |
https://doi.org/10.5194/isprs-archives-XLII-1-347-2018 |
|
dc.bibliographicCitation.issue |
1 |
|
dc.bibliographicCitation.volume |
42 |
|
dc.bibliographicCitation.firstPage |
347 |
|
dc.bibliographicCitation.lastPage |
354 |
|
dc.description.version |
publishedVersion |
|
tib.accessRights |
frei zug�nglich |
|