| Texto completo | |
| Autor(es): |
Perciano, T.
;
Tupin, F.
;
Hirata, Jr., R.
;
Cesar, Jr., R. M.
Número total de Autores: 4
|
| Tipo de documento: | Artigo Científico |
| Fonte: | International Journal of Remote Sensing; v. 37, n. 16, p. 3584-3610, AUG 20 2016. |
| Citações Web of Science: | 6 |
| Resumo | |
This article introduces a method for road network extraction from satellite images. The proposed approach covers a new fusion method (using data from multiple sources) and a new Markov random field (MRF) defined on connected components along with a multilevel application (two-level MRF). Our method allows the detection of roads with different characteristics and decreases by around 30% the size of the used graph model. Results for synthetic aperture radar (SAR) images and optical images obtained using the TerraSAR-X and Quickbird sensors, respectively, are presented demonstrating the improvement brought by the proposed approach. In a second part, an analysis of different types of data fusion combining optical/radar images, radar/radar images, and multitemporal SAR (TerraSAR-X and COSMO-SkyMed) images is described. The qualitative and quantitative results show that the fusion approach improves considerably the results of the road network extraction. (AU) | |
| Processo FAPESP: | 11/50761-2 - Modelos e métodos de e-Science para ciências da vida e agrárias |
| Beneficiário: | Roberto Marcondes Cesar Junior |
| Linha de fomento: | Auxílio à Pesquisa - Temático |