| Full text | |
| Author(s): |
Ananias, Pedro Henrique M.
;
Negri, Rogerio G.
;
Bressane, Adriano
;
Colnago, Marilaine
;
Casaca, Wallace
Total Authors: 5
|
| Document type: | Journal article |
| Source: | SOFTWARE IMPACTS; v. 15, p. 3-pg., 2023-02-17. |
| Abstract | |
This paper presents a new approach for detecting algal insurgence in water environments by using remote sensing image series. The designed methodology provides a robust and accurate algorithm as an alternative to typical algal bloom detection methods. In more technical terms, by only assuming as input an image time series, a fully automatic data-driven scheme involving pre-processing and feature extraction procedures is derived, which models a machine intelligent-based classifier capable of detecting algal blooms. Lastly, algal insurgence maps are then produced by passing to the classifier an image taken at an instant of interest. (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 |