ABD: A machine intelligent-based algal bloom detector for remote sensing images[Formula presented]
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 inp...
| Autores: | , , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2023 |
| País: | Brasil |
| Institución: | Universidade Estadual Paulista (UNESP) |
| Repositorio: | Repositório Institucional da UNESP |
| Idioma: | inglés |
| OAI Identifier: | oai:repositorio.unesp.br:11449/248380 |
| Acceso en línea: | http://dx.doi.org/10.1016/j.simpa.2023.100482 http://hdl.handle.net/11449/248380 |
| Access Level: | acceso abierto |
| Palabra clave: | Algal bloom Machine learning Remote sensing Spectral index |
| Sumario: | 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. |
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