| Full text | |
| Author(s): |
Negri, Rogerio G.
;
Luz, Andrea E. O.
;
Frery, Alejandro C.
;
Casaca, Wallace
;
IEEE
Total Authors: 5
|
| Document type: | Journal article |
| Source: | 2023 INTERNATIONAL CONFERENCE ON MACHINE INTELLIGENCE FOR GEOANALYTICS AND REMOTE SENSING, MIGARS; v. N/A, p. 4-pg., 2023-01-01. |
| Abstract | |
The frequency of forest fires has increased significantly in recent years across the planet. Events of this nature motivate the development of automated methodologies aimed at mapping areas affected by fire. In this context, we propose a method capable of accurately mapping areas affected by fire using time series of remotely sensed multispectral images by statistical modeling and classification. In order to evaluate the introduced proposal, we carry out a case study on a region in Brazil with recurrent history of forest fires. Furthermore, images obtained by the Landsat-8 satellite are used in this case study. Comparisons with an alternative method are included in this analysis. (AU) | |
| FAPESP's process: | 21/03328-3 - Development of new methodologies and machine intelligence-based technological solutions for digital image segmentation and COVID-19 pandemic response |
| Grantee: | Wallace Correa de Oliveira Casaca |
| Support Opportunities: | Regular Research Grants |
| FAPESP's process: | 21/01305-6 - Theoretical advances on anomaly detection and environmental monitoring systems building |
| Grantee: | Rogério Galante Negri |
| Support Opportunities: | Regular Research Grants |