Center Pivot Classification with Deep Residual U-NET

Center pivots are a modern irrigation technique mainly applied in precision agriculture, once it has high efficiency in water consumption and low labor workers when compared to traditional irrigation methods. Knowing their location is valuable since monitoring, evaluating, and estimating essential f...

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Published inIGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium pp. 1596 - 1599
Main Authors de Albuquerque, Anesmar Olino, de Bem, Pablo Pozzobon, dos Santos de Moura, Rebeca, de Carvalho, Osmar Luiz Ferreira, Ferreira, Pedro Henrique Guimaraes, Silva, Cristiano Rosa, Gomes, Roberto Arnaldo Trancoso, Guimaraes, Renato Fontes, de Carvalho, Osmar Abilio
Format Conference Proceeding
LanguageEnglish
Published IEEE 26.09.2020
Subjects
Artificial satellites
Center pivot
deep learning
deep residual u-net
Earth
Image segmentation
Irrigation
Remote sensing
Satellites
Training
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Summary:Center pivots are a modern irrigation technique mainly applied in precision agriculture, once it has high efficiency in water consumption and low labor workers when compared to traditional irrigation methods. Knowing their location is valuable since monitoring, evaluating, and estimating essential features in the lands becomes easier, remote sensing is a robust tool to act upon this kind of problem. To identify center pivots, we used a deep residual U-Net with a pixel comparison at image reconstruction to enhance results. We obtained a validation loss of 0.19, which adds up with pixel comparison. Results were satisfactory, with 2070 correct identifications from a total of 2109 center pivots (98.15%). Future studies to improve these results would require more data in different places and seasons.
ISSN:2153-7003
DOI:10.1109/IGARSS39084.2020.9323603