Name
Title | ||
|---|---|---|
A generalized space-time OBIA classification scheme to map sugarcane areas at regional scale, using Landsat images time-series and the random forest algorithm |
Abstract | ||
|---|---|---|
•Operational spatially generalized sugarcane classifier is crucial at regional scale.•Space and time generalization were tested for three approaches in SP State, Brazil.•Multi-site calibration from Landsat imagery performs better for mapping large areas.•Produced maps have similar precision than existing governamental statistics. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.jag.2019.04.013 | International Journal of Applied Earth Observation and Geoinformation |
Keywords | Field | DocType |
Classifier extension,Data mining,Machine learning,Sugarcane mapping | Space time,Sørensen–Dice coefficient,Generalization,Remote sensing,Classification scheme,Classifier (linguistics),Random forest,Geography,Calibration | Journal |
Volume | ISSN | Citations |
80 | 0303-2434 | 0 |
PageRank | References | Authors |
0.34 | 0 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ana Cláudia dos Santos Luciano | 1 | 0 | 0.34 |
| Michelle Cristina Araújo Picoli | 2 | 0 | 1.01 |
| Jansle Vieira Rocha | 3 | 4 | 2.46 |
| Daniel Garbellini Duft | 4 | 0 | 0.34 |
| Rubens Camargo Lamparelli | 5 | 59 | 6.52 |
| Manoel Regis Lima Verde Leal | 6 | 0 | 0.34 |
| Guerric le Maire | 7 | 31 | 6.86 |