Please use this identifier to cite or link to this item:
http://hdl.handle.net/10174/31998
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| Title: | Classifying Soil Type Using Radar Satellite Images |
| Authors: | Ahmed, MD Sajib
Gonçalves, Teresa
Rato, Luís
Marques da Silva, José Rafael
Vieira, Filipe
Paixão, Luís
Salgueiro, Pedro |
| Keywords: | Remote Sensing
Soil Electrical Conductivity
Sentinel-1, Machine Learning
Random Forest |
| Issue Date: | 30-Oct-2020 |
| Abstract: | The growth of the crop is dependent on soil type, apart from atmospheric
and geo-location characteristics. As of now, there is no direct and costfree method to measure soil property or to classify soil type. In this
work, we proposed a machine learning model to classify soil type using Sentinel-1 satellite radar images. Further, the developed classifier
achieved 72.17% F1-score classifying sandy, free and clayish on a set
of 65003 data points collected over one year (from Oct 2018 to Sep 2019)
over 14 corn parcels near Ourique, Portugal. |
| URI: | https://recpad2020.uevora.pt/wp-content/uploads/2020/11/proceedings_recpad2020.pdf
http://hdl.handle.net/10174/31998 |
| Type: | article |
| Appears in Collections: | INF - Artigos em Livros de Actas/Proceedings
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