KOPRI Repository

Digital surface model generation for drifting Arctic sea ice with low-textured surfaces based on drone images

Cited 0 time in wos
Cited 0 time in scopus
Metadata Downloads

Full metadata record

DC Field Value Language
dc.contributor.authorKim, Jae-In-
dc.contributor.authorHyun, Chang-Uk-
dc.contributor.authorHan, Hyangsun-
dc.contributor.authorKim, Hyun-cheol-
dc.date.accessioned2021-04-30T00:43:27Z-
dc.date.available2021-04-30T00:43:27Z-
dc.date.issued2021-02-
dc.identifier.urihttps://repository.kopri.re.kr/handle/201206/11784-
dc.description.abstractArctic sea ice is constantly moving and covered with low-textured surfaces, making it difficult to generate reliable digital surface models (DSMs) from drone images. The movement of sea ice makes georeferencing of DSMs difficult, and the low-textured surfaces of sea ice cause the uncertainty of image matching. This paper proposes a robust method to generate high-quality DSMs for drifting sea ice. To overcome the challenges, the proposed method introduces four improvements to the object-space-based image-matching pipeline: relative georeferencing to recover the horizontality and scale of sea-ice DSMs using a terrestrial light detection and ranging (LiDAR) dataset, match inspection to verify the matched points using several matching constraints, adaptive search-window adjustment to ensure distinct texture information through simple texture analysis, and robust vertical positioning to reduce the matching uncertainty via matching-indicator modeling. Performance evaluations were conducted with drone and LiDAR datasets obtained from a sea-ice campaign using the Korean Icebreaker Research Vessel (IBRV) Araon in the summer of 2017. The experimental results indicated that the proposed method can achieve significant quality enhancements compared with the existing matching method and that all the considerations contributed significantly to the enhancements.en_US
dc.languageEnglishen_US
dc.subjectPhysical Geographyen_US
dc.subjectGeologyen_US
dc.subjectRemote Sensingen_US
dc.subjectImaging Science & Photographic Technologyen_US
dc.subject.classificationAraonen_US
dc.titleDigital surface model generation for drifting Arctic sea ice with low-textured surfaces based on drone imagesen_US
dc.title.alternative표면 텍스쳐가 적은 부유 해빙에 대한 드론영상 기반 수치표고모델 생성en_US
dc.typeArticleen_US
dc.identifier.bibliographicCitationKim, Jae-In, et al. 2021. "Digital surface model generation for drifting Arctic sea ice with low-textured surfaces based on drone images". <em>ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING</em>, 172(2021): 147-159.en_US
dc.citation.titleISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSINGen_US
dc.citation.volume172en_US
dc.citation.number2021en_US
dc.identifier.doi10.1016/j.isprsjprs.2020.12.008-
dc.citation.startPage147en_US
dc.citation.endPage159en_US
dc.description.articleClassificationSCIE-
dc.description.jcrRateJCR 2019:2en_US
dc.subject.keywordSea iceen_US
dc.subject.keywordDigital surface modelen_US
dc.subject.keywordUnmanned aerial vehicleen_US
dc.subject.keywordImage matchingen_US
dc.subject.keywordGeoreferencingen_US
dc.subject.keywordLow-textured surfaceen_US
dc.identifier.localId2021-0002-
dc.identifier.scopusid2-s2.0-85098728703-
dc.identifier.wosid000610187300010-
Appears in Collections  
2020-2020, Study on remote sensing for quantitative analysis of changes in the Arctic cryosphere (20-20) / Kim, Hyun-cheol (PE20080)
Files in This Item

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

Browse