Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/89011
DC Field | Value | Language |
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dc.contributor | Department of Land Surveying and Geo-Informatics | en_US |
dc.creator | Polewski, P | en_US |
dc.creator | Shelton, J | en_US |
dc.creator | Yao, W | en_US |
dc.creator | Heurich, M | en_US |
dc.date.accessioned | 2021-01-15T07:14:47Z | - |
dc.date.available | 2021-01-15T07:14:47Z | - |
dc.identifier.issn | 1682-1750 | en_US |
dc.identifier.uri | http://hdl.handle.net/10397/89011 | - |
dc.description | 2020 24th ISPRS Congress - Technical Commission II, 31 August - 2 September 2020 | en_US |
dc.language.iso | en | en_US |
dc.publisher | Copernicus GmbH | en_US |
dc.rights | © Author(s) 2020. This work is distributed under the Creative Commons Attribution 4.0 License (https://creativecommons.org/licenses/by/4.0/). | en_US |
dc.rights | The following publication Polewski, P., Shelton, J., Yao, W., and Heurich, M.: SEGMENTATION OF SINGLE STANDING DEAD TREES IN HIGH-RESOLUTION AERIAL IMAGERY WITH GENERATIVE ADVERSARIAL NETWORK-BASED SHAPE PRIORS, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2020, 717–723, is available at https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-717-2020, 2020 | en_US |
dc.subject | Active contour | en_US |
dc.subject | CNN | en_US |
dc.subject | Color infrared | en_US |
dc.subject | Forest health monitoring | en_US |
dc.subject | Gan | en_US |
dc.title | Segmentation of single standing dead trees in high-resolution aerial imagery with generative adversarial network-based shape priors | en_US |
dc.type | Conference Paper | en_US |
dc.identifier.spage | 717 | en_US |
dc.identifier.epage | 723 | en_US |
dc.identifier.volume | 43 | en_US |
dc.identifier.issue | B2 | en_US |
dc.identifier.doi | 10.5194/isprs-archives-XLIII-B2-2020-717-2020 | en_US |
dcterms.abstract | The use of multispectral imagery for monitoring biodiversity in ecosystems is becoming widespread. A key parameter of forest ecosystems is the distribution of dead wood. This work addresses the segmentation of individual dead tree crowns in nadir-view aerial infrared imagery. While dead vegetation produces a distinct spectral response in the near infrared band, separating adjacent trees within large swaths of dead stands remains a challenge. We tackle this problem by casting the segmentation task within the active contour framework, a mathematical formulation combining learned models of the object's shape and appearance as prior information. We explore the use of a deep convolutional generative adversarial network (DCGAN) in the role of the shape model, replacing the original linear mixture-of-eigenshapes formulation. Also, we rely on probabilities obtained from a deep fully convolutional network (FCN) as the appearance prior. Experiments conducted on manually labeled reference polygons show that the DCGAN is able to learn a low-dimensional manifold of tree crown shapes, outperforming the eigenshape model with respect to the similarity of the reproduced and referenced shapes on about 45 % of the test samples. The DCGAN is successful mostly for less convex shapes, whereas the baseline remains superior for more regular tree crown polygons. | en_US |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | International archives of the photogrammetry, remote sensing and spatial information sciences, 12 Aug. 2020, v. 43, no. B2, p. 717-723 | en_US |
dcterms.isPartOf | International archives of the photogrammetry, remote sensing and spatial information sciences | en_US |
dcterms.issued | 2020-08-12 | - |
dc.identifier.scopus | 2-s2.0-85091078721 | - |
dc.relation.conference | ISPRS Congress on Technical Commission | en_US |
dc.identifier.eissn | 2194-9034 | en_US |
dc.description.validate | 202101 bcrc | en_US |
dc.description.oa | Version of Record | en_US |
dc.identifier.FolderNumber | a0616-n01, OA_Scopus/WOS | en_US |
dc.identifier.SubFormID | 606 | - |
dc.description.fundingSource | Self-funded | en_US |
dc.description.pubStatus | Published | en_US |
dc.description.oaCategory | CC | en_US |
Appears in Collections: | Conference Paper |
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File | Description | Size | Format | |
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Polewski_Segmentation_single_standing.pdf | 1.36 MB | Adobe PDF | View/Open |
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