Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/91043
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dc.contributorDepartment of Land Surveying and Geo-Informaticsen_US
dc.creatorPolewski, Pen_US
dc.creatorShelton, Jen_US
dc.creatorYao, Wen_US
dc.creatorHeurich, Men_US
dc.date.accessioned2021-09-09T03:38:26Z-
dc.date.available2021-09-09T03:38:26Z-
dc.identifier.issn0924-2716en_US
dc.identifier.urihttp://hdl.handle.net/10397/91043-
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.subjectEnergy minimizationen_US
dc.subjectPrecision forestryen_US
dc.subjectSample consensusen_US
dc.subjectSimulated annealingen_US
dc.subjectU-neten_US
dc.titleInstance segmentation of fallen trees in aerial color infrared imagery using active multi-contour evolution with fully convolutional network-based intensity priorsen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage297en_US
dc.identifier.epage313en_US
dc.identifier.volume178en_US
dc.identifier.doi10.1016/j.isprsjprs.2021.06.016en_US
dcterms.abstractOver 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.en_US
dcterms.accessRightsembargoed accessen_US
dcterms.bibliographicCitationISPRS Journal of Photogrammetry and Remote Sensing, Aug. 2021, v. 178, p. 297-313en_US
dcterms.isPartOfISPRS journal of photogrammetry and remote sensingen_US
dcterms.issued2021-08-
dc.identifier.scopus2-s2.0-85109099799-
dc.description.validate202109 bchyen_US
dc.description.oaNot applicableen_US
dc.identifier.FolderNumbera1024-n01-
dc.identifier.SubFormID2448-
dc.description.fundingSourceRGCen_US
dc.description.fundingSourceOthersen_US
dc.description.fundingTextRGC: PolyU 25604917en_US
dc.description.fundingTextOthers: 1-ZE8E, G-YBZ9en_US
dc.description.pubStatusPublisheden_US
dc.date.embargo2023-08-31en_US
Appears in Collections:Journal/Magazine Article
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Embargo End Date 2023-08-31
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