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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