| Full text | |
| Author(s): |
Soares, Anderson R.
;
Bendini, Hugo N.
;
Vaz, Daiane V.
;
Uehara, Tatiana D. T.
;
Neves, Alana K.
;
Lechler, Sarah
;
Korting, Thales S.
;
Fonseca, Leila M. G.
;
IEEE
Total Authors: 9
|
| Document type: | Journal article |
| Source: | IGARSS 2020 - 2020 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM; v. N/A, p. 4-pg., 2020-01-01. |
| Abstract | |
Producing reliable land use and land cover maps to support the deployment and operation of public policies is a necessity, especially when environmental management and economic development are considered. To increase the accuracy of these maps, satellite image time-series have been used, as they allow the understanding of land cover dynamics through the time. This paper presents the stmetrics, a python package that provides the extraction of state-of-the-art time-series features. These features can be used for remote sensing time-series image classification and analysis. stmetrics aims to be an easy-to-use package. The package is available under the GNU GPL software license, and the full source code is available for download at: github.com/andersonreisoares/stmetrics. (AU) | |
| FAPESP's process: | 17/24086-2 - Management of metadata from remote sensing big data |
| Grantee: | Thales Sehn Körting |
| Support Opportunities: | Regular Research Grants |