| Texto completo | |
| Autor(es): |
Neves, Alana Kasahara
;
Korting, Thales Sehn
;
Neto, Cesare Di Girolamo
;
Soares, Anderson Reis
;
Garcia Fonseca, Leila Maria
;
IEEE
Número total de Autores: 6
|
| Tipo de documento: | Artigo Científico |
| Fonte: | 2019 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS 2019); v. N/A, p. 4-pg., 2019-01-01. |
| Resumo | |
An accurate mapping of Brazilian Savanna (Cerrado) is still a difficult task due to the high spatial variability and spectral similarity between its vegetation types, called physiognomies. This work proposes a methodology based on the hierarchy of physiognomies, GEOBIA techniques with Super-pixel and a very high spatial resolution image (WorldView2) to classify the Cerrado physiognomies in an area of preserved vegetation. Seven classes were distinguished: Gallery Forest, Wooded Savanna, Typical Savanna, Shrub Savanna, Shrub Grassland, Open Grassland and Rocky Grassland. The texture features were essential for the classification and the hierarchical approach obtained higher accuracies than the non-hierarchical approach. Moreover, GEOBIA and Superpixel were essential to represent the context that characterizes each physiognomy. (AU) | |
| Processo FAPESP: | 17/24086-2 - Gerenciamento de metadados de grandes volumes de dados de sensoriamento remoto |
| Beneficiário: | Thales Sehn Körting |
| Modalidade de apoio: | Auxílio à Pesquisa - Regular |