| Full text | |
| Author(s): |
Perciano, T.
;
Tupin, F.
;
Hirata, Jr., R.
;
Cesar, Jr., R. M.
Total Authors: 4
|
| Document type: | Journal article |
| Source: | International Journal of Remote Sensing; v. 37, n. 16, p. 3584-3610, AUG 20 2016. |
| Web of Science Citations: | 6 |
| Abstract | |
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) | |
| FAPESP's process: | 11/50761-2 - Models and methods of e-Science for life and agricultural sciences |
| Grantee: | Roberto Marcondes Cesar Junior |
| Support Opportunities: | Research Projects - Thematic Grants |