Web of Science: 4 cites, Scopus: 4 cites, Google Scholar: cites,
A framework of filtering rules over ground truth samples to achieve higher accuracy in land cover maps
Padial-Iglesias, Mario (Universitat Autònoma de Barcelona. Departament de Geografia)
Serra Ruiz, Pere (Universitat Autònoma de Barcelona. Departament de Geografia)
Ninyerola i Casals, Miquel (Universitat Autònoma de Barcelona. Departament de Biologia Animal, de Biologia Vegetal i d'Ecologia)
Pons, Xavier (Universitat Autònoma de Barcelona. Departament de Geografia)

Data: 2021
Resum: Remote Sensing (RS) digital classification techniques require sufficient, accurate and ubiquitously distributed ground truth (GT) samples. GT is usually considered "true" per se; however, human errors, or differences in criteria when defining classes, among other reasons, often undermine this veracity. Trusting the GT is so crucial that protocols should be defined for making additional quality checks before passing to the classification stage. Fortunately, the nature of RS imagery allows setting a framework of quality controls to improve the confidence in the GT areas by proposing a set of filtering rules based on data from the images themselves. In our experiment, two pre-existing reference datasets (rDS) were used to obtain GT candidate pixels, over which inconsistencies were identified. This served as a basis for inferring five key filtering rules based on NDVI data, a product available from almost all RS instruments. We evaluated the performance of the rules in four temporal study cases (under backdating and updating scenarios) and two study areas. In each case, a set of GT samples was extracted from the rDS and the set was used both unfiltered (original) and filtered according to the rules. Our proposal shows that the filtered GT samples made it possible to solve usual problems in wilderness and agricultural categories. Indeed, the confusion matrices revealed, on average, an increase in the overall accuracy of 10. 9, a decrease in the omission error of 16. 8, and a decrease in the commission error of 14. 0, all values in percent points. Filtering rules corrected inconsistencies in the GT samples extracted from the rDS by considering inter-annual and intra-annual differences, scale issues, multiple behaviours over time and labelling misassignments. Therefore, although some intrinsic limitations have been detected (as in mixed forests), the protocol allows a much better Land Cover mapping thanks to using more robust GT samples, something particularly important in a multitemporal context in which accounting for phenology is essential.
Ajuts: Ministerio de Ciencia e Innovación BES-2016-078262
Agencia Estatal de Investigación RTI2018-099397-B-C21
Drets: Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original. Creative Commons
Llengua: Anglès
Document: Article ; recerca ; Versió publicada
Matèria: Land-cover change mapping ; Landsat ; Digital image classification ; Ground truth samples ; Filtering rules
Publicat a: Remote sensing (Basel), Vol. 13, Issue 14 (July 2021) , art. 2662, ISSN 2072-4292

DOI: 10.3390/rs13142662


36 p, 19.5 MB

El registre apareix a les col·leccions:
Documents de recerca > Documents dels grups de recerca de la UAB > Centres i grups de recerca (producció científica) > Ciències > Grup de Recerca Mètodes i Aplicacions en Teledetecció i Sistemes d'Informació Geogràfica (GRUMETS)
Articles > Articles de recerca
Articles > Articles publicats

 Registre creat el 2022-01-20, darrera modificació el 2023-06-12



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