Details
| Originalsprache | Englisch |
|---|---|
| Seiten (von - bis) | 25-33 |
| Seitenumfang | 9 |
| Fachzeitschrift | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Jahrgang | 5 |
| Ausgabenummer | 2 |
| Publikationsstatus | Veröffentlicht - 3 Aug. 2020 |
| Veranstaltung | 2020 24th ISPRS Congress on Technical Commission II - Nice, Virtual, Frankreich Dauer: 31 Aug. 2020 → 2 Sept. 2020 |
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.
ASJC Scopus Sachgebiete
- Erdkunde und Planetologie (insg.)
- Erdkunde und Planetologie (sonstige)
- Umweltwissenschaften (insg.)
- Umweltwissenschaften (sonstige)
- Physik und Astronomie (insg.)
- Instrumentierung
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in: ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Jahrgang 5, Nr. 2, 03.08.2020, S. 25-33.
Publikation: Beitrag in Fachzeitschrift › Konferenzaufsatz in Fachzeitschrift › Forschung › Peer-Review
}
TY - JOUR
T1 - Deep Learning Based Feature Matching and Its Application in Image Orientation
AU - Chen, L.
AU - Rottensteiner, F.
AU - Heipke, C.
N1 - Funding Information: The authors would like to thank NVIDIA Corp. for donating the GPU used in this research through its GPU grant program.
PY - 2020/8/3
Y1 - 2020/8/3
N2 - 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.
AB - 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.
KW - Affine Shape Estimation
KW - Descriptor Learning
KW - Feature Orientation
KW - Image Matching
KW - Image Orientation
UR - http://www.scopus.com/inward/record.url?scp=85091092651&partnerID=8YFLogxK
U2 - 10.5194/isprs-annals-V-2-2020-25-2020
DO - 10.5194/isprs-annals-V-2-2020-25-2020
M3 - Conference article
AN - SCOPUS:85091092651
VL - 5
SP - 25
EP - 33
JO - ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
JF - ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
SN - 2194-9042
IS - 2
T2 - 2020 24th ISPRS Congress on Technical Commission II
Y2 - 31 August 2020 through 2 September 2020
ER -