A3 Kirjan tai muun kokoomateoksen osa
Using Aerial Platforms in Predicting Water Quality Parameters from Hyperspectral Imaging Data with Deep Neural Networks (2020)

Hakala, T., Pölönen, I., Honkavaara, E., Näsi, R., Hakala, T., & Lindfors, A. (2020). Using Aerial Platforms in Predicting Water Quality Parameters from Hyperspectral Imaging Data with Deep Neural Networks. In P. Diez, P. Neittaanmäki, J. Periaux, T. Tuovinen, & J. Pons-Prats (Eds.), Computation and Big Data for Transport : Digital Innovations in Surface and Air Transport Systems (pp. 213-238). Springer. Computational Methods in Applied Sciences, 54. https://doi.org/10.1007/978-3-030-37752-6_13

JYU-tekijät tai -toimittajat

Julkaisun tiedot

Julkaisun kaikki tekijät tai toimittajat: Hakala, Taina; Pölönen, Ilkka; Honkavaara, Eija; Näsi, Roope; Hakala, Teemu; Lindfors, Antti

Emojulkaisu: Computation and Big Data for Transport : Digital Innovations in Surface and Air Transport Systems

Emojulkaisun toimittajat: Diez, Pedro; Neittaanmäki, Pekka; Periaux, Jacques; Tuovinen, Tero; Pons-Prats, Jordi

ISBN: 978-3-030-37751-9

eISBN: 978-3-030-37752-6

Lehti tai sarja: Computational Methods in Applied Sciences

ISSN: 1871-3033

Julkaisuvuosi: 2020

Sarjan numero: 54

Artikkelin sivunumerot: 213-238

Kirjan kokonaissivumäärä: 250

Kustantaja: Springer

Kustannuspaikka: Cham

Julkaisumaa: Sveitsi

Julkaisun kieli: englanti

DOI: https://doi.org/10.1007/978-3-030-37752-6_13

Julkaisun avoin saatavuus: Ei avoin

Julkaisukanavan avoin saatavuus: 

Julkaisu on rinnakkaistallennettu (JYX): https://jyx.jyu.fi/handle/123456789/74035

Tiivistelmä

In near future it is assumable that automated unmanned aerial platforms are coming more common. There are visions that transportation of different goods would be done with large planes, which can handle over 1000 kg payloads. While these planes are used for transportation they could similarly be used for remote sensing applications by adding sensors to the planes. Hyperspectral imagers are one this kind of sensor types. There is need for the efficient methods to interpret hyperspectral data to the wanted water quality parameters. In this work we survey the performance of neural networks in the prediction of water quality parameters from remotely sensed hyperspectral data in freshwater basins. The hyperspectral data consists of 36 bands in the wavelength range of 508–878 nm and the water quality parameters to be predicted are temperature, conductivity, turbidity, Secchi depth, blue-green algae, chlorophyll-a, total phosphorus, acidity and dissolved oxygen. The objective of this investigation was to study the behaviour of different types of neural networks with this kind of data. Study is a survey of the operation of neural networks on this problem, which can be used as a basis for the design of a more comprehensive study. The neural network types examined were multilayer perceptron and 1-, 2- and 3-dimensional convolutional neural networks with the effect of scaling the hyperspectral data with standard or min-max -scaler recorded. We also investigated investigated how the prediction of individual water quality parameter depends on whether the neural network model is done solely with respect to this one parameter or with several parameters predicted simultaneously with the same model. The results of the correspondence between the predicted and measured water quality parameters were presented with normalized root mean square error, Pearson correlation coefficient and coefficient of determination. The best models were obtained the 2-dimensional convolutional neural networks with standard scaling made separately for each parameter. The parameters showing good predictability were conductivity, turbidity, Secchi-depth, blue-green algae, chlorophyll-a and total phosphorus, for which the coefficient of determination was at least 0.96 (apart from Secchi-depth even 0.98).

YSO-asiasanat: kaukokartoitus; ilmakuvakartoitus; spektrikuvaus; vedenlaatu; vesien tila; neuroverkot

Vapaat asiasanat: remote sensing; hyperspectral; water quality; convolutional neural networks

Liittyvät organisaatiot

OKM-raportointi: Kyllä

Raportointivuosi: 2020

JUFO-taso: 0