We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Mapeando usos/coberturas da terra com Semi-automatic Classification Plugin: quais dados, classificador e estratégia amostral?
- Authors
Flávio PEREIRA, Luís; GUIMARÃE, Ricardo Morato Fiúza
- Abstract
This paper aimed to suggest guidelines to better map land uses using the Semi-automatic Classification Plugin (SCP) for QGIS, highlighting which the best data sets, classifiers and training sampling designs. Four data sets from a Sentinel 2A image were combined with three classifiers available in the SCP, and two sampling designs: separate or dissolved training samples (ROI's) in a single sample, obtaining 24 treatments. The treatments were evaluated regarding the accuracy (Kappa coefficient), visual quality of the final map and processing time. The results suggest that: (1) the SCP is suitable to map land uses; (2) the larger the data set, the better the classifier performance; and (3) the use of dissolved ROI always decreases processing time, but has an ambiguous effect on the different classifiers. In order to get better results, we recommend to apply the Maximum Likelihood classifier on the largest data set available, using training samples that cover all possible intraclass variations, subsequently dissolved in a single ROI.
- Publication
Nativa, 2019, Vol 7, Issue 1, p70
- ISSN
2318-7670
- Publication type
Article
- DOI
10.31413/nativa.v7i1.6845