Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/91043
| Title: | Instance segmentation of fallen trees in aerial color infrared imagery using active multi-contour evolution with fully convolutional network-based intensity priors | Authors: | Polewski, P Shelton, J Yao, W Heurich, M |
Issue Date: | Aug-2021 | Source: | ISPRS Journal of Photogrammetry and Remote Sensing, Aug. 2021, v. 178, p. 297-313 | Abstract: | Over the last several years, semantic image segmentation based on deep neural networks has been greatly advanced. On the other hand, single-instance segmentation still remains a challenging problem. In this paper, we introduce a framework for segmenting instances of a common object class by multiple active contour evolution over semantic segmentation maps of images obtained through fully convolutional networks. The contour evolution is cast as an energy minimization problem, where the aggregate energy functional incorporates a data fit term, an explicit shape model, and accounts for object overlap. Efficient solution neighborhood operators are proposed, enabling optimization through metaheuristics such as simulated annealing. We instantiate the proposed framework in the context of segmenting individual fallen stems from high-resolution aerial multispectral imagery, providing problem-specific energy potentials. We validated our approach on 3 real-world scenes of varying complexity, using 730 manually labeled polygon outlines as ground truth. The test plots were situated in regions of the Bavarian Forest National Park, Germany, which sustained a heavy bark beetle infestation. Evaluations were performed on both the polygon and line segment level, showing that the multi-contour segmentation can achieve up to 0.93 precision and 0.82 recall. An improvement of up to 7 percentage points (pp) in recall and 6 in precision compared to an iterative sample consensus line segment detection baseline was achieved. Despite the simplicity of the applied shape parametrization, an explicit shape model incorporated into the energy function improved the results by up to 4 pp of recall. Finally, we show the importance of using a high-quality semantic segmentation method (e.g. U-net) as the basis for individual stem detection, as the quality of the results degraded dramatically in our baseline experiment utilizing a simpler method. Our method is a step towards increased accessibility of automatic fallen tree mapping in forests, due to higher cost efficiency of aerial imagery acquisition compared to laser scanning. The precise fallen tree maps could be further used as a basis for plant and animal habitat modeling, studies on carbon sequestration as well as soil quality in forest ecosystems. | Keywords: | Energy minimization Precision forestry Sample consensus Simulated annealing U-net |
Publisher: | Elsevier | Journal: | ISPRS journal of photogrammetry and remote sensing | ISSN: | 0924-2716 | DOI: | 10.1016/j.isprsjprs.2021.06.016 |
| Appears in Collections: | Journal/Magazine Article |
Access
View full-text via PolyU eLinks 
Page views
29
Last Week
0
0
Last month
Citations as of Jun 4, 2023
SCOPUSTM
Citations
6
Citations as of Jun 8, 2023
WEB OF SCIENCETM
Citations
6
Citations as of Jun 8, 2023
Google ScholarTM
Check
Altmetric
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.