Details
| Original language | English |
|---|---|
| Title of host publication | Beiträge |
| Subtitle of host publication | 39. Wissenschaftlich-Technische Jahrestagung der DGPF e.V. |
| Editors | Thomas P. Kersten |
| Place of Publication | München |
| Pages | 375-386 |
| Number of pages | 12 |
| Publication status | Published - 2019 |
| Event | Dreiländertagung OVG-DGPF-SGPF: Photogrammetrie-Fernerkundung-Geoinformation - Universität für Bodenkultur Wien, Wien, Austria Duration: 20 Feb 2019 → 22 Feb 2019 https://dgpf.de/con/jt2019.html |
Publication series
| Name | Publikationen der Deutschen Gesellschaft für Photogrammetrie, Fernerkundung und Geoinformation e.V. |
|---|---|
| Publisher | Deutsche Gesellschaft für Photogrammetrie, Fernerkundung und Geoinformation e.V. |
| Volume | 28 |
| ISSN (Print) | 0942-2870 |
Abstract
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Beiträge: 39. Wissenschaftlich-Technische Jahrestagung der DGPF e.V.. ed. / Thomas P. Kersten. München, 2019. p. 375-386 (Publikationen der Deutschen Gesellschaft für Photogrammetrie, Fernerkundung und Geoinformation e.V.; Vol. 28).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research
}
TY - GEN
T1 - Complementary Features Learning from RGB and Depth Information for Semantic Image Labelling
AU - Chen, Lin
AU - Zhao, Daixin
AU - Heipke, Christian
N1 - Funding Information: Part of this work is done by DAIXIN ZHAO during an internship at Institute of Photogrammetry and GeoInformation, Leibniz Universität Hannover. The authors LIN CHEN and CHRISTIAN HEIPKE are grateful to NVIDIA Corp. for the GPU donation. Also, the author LIN CHEN and DAIXIN ZHAO are grateful to CHUN YANG and JUNHUA KANG for valuable discussions.
PY - 2019
Y1 - 2019
N2 - In this paper, we present a complementarity constraint for features computed from different sources of input data before fusion in semantic labelling. A two-branch encoder-decoder architecture with ResNet-50 is proposed and used as classification network. Our proposed complementarity constraint is added to the standard softmax cross-entropy classi-fication loss. The impact of different weights for this constraint in multi-modal data fusion is investigated. The result of the two branch network is also compared to the one obtained with only the spectral information. The constraint is shown to improve the results consistently in our experiments. Different amounts of improvement are achieved when different weighs for the complementarity constraint are used.
AB - In this paper, we present a complementarity constraint for features computed from different sources of input data before fusion in semantic labelling. A two-branch encoder-decoder architecture with ResNet-50 is proposed and used as classification network. Our proposed complementarity constraint is added to the standard softmax cross-entropy classi-fication loss. The impact of different weights for this constraint in multi-modal data fusion is investigated. The result of the two branch network is also compared to the one obtained with only the spectral information. The constraint is shown to improve the results consistently in our experiments. Different amounts of improvement are achieved when different weighs for the complementarity constraint are used.
M3 - Conference contribution
T3 - Publikationen der Deutschen Gesellschaft für Photogrammetrie, Fernerkundung und Geoinformation e.V.
SP - 375
EP - 386
BT - Beiträge
A2 - Kersten, Thomas P.
CY - München
T2 - Dreiländertagung OVG-DGPF-SGPF
Y2 - 20 February 2019 through 22 February 2019
ER -