ABD: A machine intelligent-based algal bloom detector for remote sensing images[Formula presented]

• Autor(a) principal: Ananias, Pedro Henrique M. [UNESP]
• Data de Publicação: 2023
• Outros Autores: Negri, Rogério G. [UNESP], Bressane, Adriano [UNESP], Colnago, Marilaine [UNESP], Casaca, Wallace [UNESP]
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.1016/j.simpa.2023.100482; http://hdl.handle.net/11449/248380
• topic: Algal bloom; Machine learning; Remote sensing; Spectral index

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.