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Title: | Segmentation of single standing dead trees in high-resolution aerial imagery with generative adversarial network-based shape priors | Authors: | Polewski, P Shelton, J Yao, W Heurich, M |
Issue Date: | 12-Aug-2020 | Source: | International archives of the photogrammetry, remote sensing and spatial information sciences, 12 Aug. 2020, v. 43, no. B2, p. 717-723 | 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. | Keywords: | Active contour CNN Color infrared Forest health monitoring Gan |
Publisher: | Copernicus GmbH | Journal: | International archives of the photogrammetry, remote sensing and spatial information sciences | ISSN: | 1682-1750 | EISSN: | 2194-9034 | DOI: | 10.5194/isprs-archives-XLIII-B2-2020-717-2020 | Description: | 2020 24th ISPRS Congress - Technical Commission II, 31 August - 2 September 2020 | Rights: | © Author(s) 2020. This work is distributed under the Creative Commons Attribution 4.0 License (https://creativecommons.org/licenses/by/4.0/). 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 |
Appears in Collections: | Conference Paper |
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Polewski_Segmentation_single_standing.pdf | 1.36 MB | Adobe PDF | View/Open |
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