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Using LUCAS survey and Recurrent Neural Networks to produce LCLU classification based on a Satellite Image time series of Sentinel-2
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Orientador(es)
Resumo(s)
The need of timely and accurate information for the territory has increased over the years, making
Land Cover Land Use (LCLU) mapping one of the most common application of remote sensing.
Recently, the advances in satellite technology and the open access policies for remote sensing data
increased the interest in exploring satellite image time series. In addition, the attention of
researchers has shifted from standard machine learning algorithms (e.g., Support Vector Machines
and Random Forest) to Recurrent Neural Networks due to their ability of exploiting sequential
information. However, acquiring reference data to train these algorithms is still a hurdle. This study
aims to evaluate the capability of a Gated Recurrent Unit in performing pixel-level LCLU classification
of a satellite image time series, using Sentinel-2 imagery and having the LUCAS survey as reference
data. To assess the performance of our model we compared it to state-of-the-art classifiers (SVM and
RF). Due to the unbalance nature of the LUCAS survey, we applied oversampling to this dataset to
increase the performance of our models, testing three different oversampling techniques. The results
attained showed that Recurrent Neural Networks did not outperform the other state-of-the-art
algorithms, when trained with a limited number of sampling units, and that oversampling the LUCAS
survey increased the performance of all the classifiers. Finally, we were able to demonstrate that it is
possible to produce LCLU classification of satellite image time series using only open-source data by
using Sentinel-2 imagery and the LUCAS survey as refence data.
Descrição
Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business Intelligence
This work was supported by national funds through FCT (Fundação para a Ciência e a Tecnologia), under the project foRESTER (PCIF/SSI/0102/2017): https://doi.org/10.54499/PCIF/SSI/0102/2017
This work was supported by national funds through FCT (Fundação para a Ciência e a Tecnologia), under the project foRESTER (PCIF/SSI/0102/2017): https://doi.org/10.54499/PCIF/SSI/0102/2017
Palavras-chave
LCLU classification LUCAS survey Recurrent Neural Networks Oversampling Sentinel-2