| Autor: |
Polewski, P., Shelton, J., Yao, W., Heurich, M. |
| Předmět: |
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| Zdroj: |
International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences; 8/12/2020, Vol. 43 Issue B2, p717-723, 7p |
| Abstrakt: |
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. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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