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| Natura: | Artículo científico |
| Lingua: | en |
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Academia Brasileira de Ciências
2013
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| Accesso online: | https://www.redalyc.org/articulo.oa?id=32727844007 |
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Sommario:
- Chlorophyll a spatial inference using artificial neural network from multispectral images and in situ measurements MONIQUE S. FERREIRA MARIA DE LOURDES B.T. GALO Multidisciplinaria (Ciencias Naturales y Exactas) fluorescence chlorophyll a spatial inference Remote sensing of water artifi cial neural network Considering the importance of monitoring the water quality parameters, remote sensing is a practicable alternative to limnological variables detection, which interacts with electromagnetic radiation, called optically active components (OAC). Among these, the phytoplankton pigment chlorophyll a is the most representative pigment of photosynthetic activity in all classes of algae. In this sense, this work aims to develop a method of spatial inference of chlorophyll a concentration using Artifi cial Neural Networks (ANN). To achieve this purpose, a multispectral image and fl uorometric measurements were used as input data. The multispectral image was processed and the net training and validation dataset were carefully chosen. From this, the neural net architecture and its parameters were defi ned to model the variable of interest. In the end of training phase, the trained network was applied to the image and a qualitative analysis was done. Thus, it was noticed that the integration of fl uorometric and multispectral data provided good results in the chlorophyll a inference, when combined in a structure of artifi cial neural networks. 2013 artículo científico 0001-3765 https://www.redalyc.org/articulo.oa?id=32727844007 en http://www.redalyc.org/revista.oa?id=327 Anais da Academia Brasileira de Ciências application/pdf Academia Brasileira de Ciências Anais da Academia Brasileira de Ciências (Brasil) Num.2 Vol.85