Analysis-ready satellite data mosaics from Landsat and Sentinel-2 imagery.

Earth observation Geomosaic - analysis-ready satellite data mosaics Land cover classification Optical satellite imagery Preprocessing Remote sensing

Journal

MethodsX
ISSN: 2215-0161
Titre abrégé: MethodsX
Pays: Netherlands
ID NLM: 101639829

Informations de publication

Date de publication:
2023
Historique:
received: 07 02 2022
accepted: 31 12 2022
entrez: 24 1 2023
pubmed: 25 1 2023
medline: 25 1 2023
Statut: epublish

Résumé

Today's enormous amounts of freely available high-resolution satellite imagery provide the demand for effective preprocessing methods. One such preprocessing method needed in many applications utilizing optical satellite imagery from the Landsat and Sentinel-2 archives is mosaicking. Merging hundreds of single scenes into a single satellite data mosaic before conducting analysis such as land cover classification, change detection, or modelling is often a prerequisite. Maintaining the original data structure and preserving metadata for further modelling or classification would be advantageous for many applications. Furthermore, in other applications, e.g., connected to land cover classification creating the mosaic for a specific period matching the phenological state of the phenomena in nature would be beneficial. In addition, supporting in-house and computing centers not directly connected to a specific cloud provider could be a requirement for some institutions or companies. In the current work, we present a method called Geomosaic that meets these criteria and produces analysis-ready satellite data mosaics from Landsat and Sentinel-2 imagery.•The method described produces analysis-ready satellite data mosaics.•The satellite data mosaics contain pixel metadata usable for further analysis.•The algorithm is available as an open-source tool coded in Python and can be used on multiple platforms.

Identifiants

pubmed: 36691672
doi: 10.1016/j.mex.2022.101995
pii: S2215-0161(22)00369-7
pmc: PMC9860476
doi:

Types de publication

Journal Article

Langues

eng

Pagination

101995

Informations de copyright

© 2023 The Author(s).

Déclaration de conflit d'intérêts

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Références

IEEE Trans Pattern Anal Mach Intell. 2006 May;28(5):673-83
pubmed: 16640255
Science. 2008 May 23;320(5879):1011
pubmed: 18497274

Auteurs

Hans Ole Ørka (HO)

Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, P.O. Box 5003, Ås NO-1432, Norway.

Jãnis Gailis (J)

Science [&] Technology Corporation, MESH, Tordenskioldsgate 6, Oslo NO-0160, Norway.

Mathias Vege (M)

Science [&] Technology Corporation, MESH, Tordenskioldsgate 6, Oslo NO-0160, Norway.

Terje Gobakken (T)

Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, P.O. Box 5003, Ås NO-1432, Norway.

Kenneth Hauglund (K)

Science [&] Technology Corporation, MESH, Tordenskioldsgate 6, Oslo NO-0160, Norway.

Classifications MeSH