CENTER PIVOT CLASSIFICATION WITH DEEP RESIDUAL U-NET

被引：0

作者：

de Albuquerque, Anesmar Olino ^{[1
]}

de Bem, Pablo Pozzobon ^{[1
]}

de Moura, Rebeca dos Santos ^{[1
]}

Ferreira de Carvalho, Osmar Luiz ^{[1
]}

Guimaraes Ferreira, Pedro Henrique ^{[1
]}

Silva, Cristiano Rosa ^{[1
]}

Trancoso Gomes, Roberto Arnaldo ^{[1
]}

Guimaraes, Renato Fontes ^{[1
]}

de Carvalho Junior, Osmar Abilio ^{[1
]}

机构：

[1] Univ Brasilia, Lab Sistemas Informacoes Espaciais LSIE, Campus Univ Darcy Ribeiro, BR-70910900 Brasilia, DF, Brazil

来源：

IGARSS 2020 - 2020 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM | 2020年

关键词：

Center pivot; deep learning; deep residual u-net; remote sensing; WATER; CONSUMPTION;

D O I：

10.1109/IGARSS39084.2020.9323603

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Center pivots are a modem 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.

引用

页码：1596 / 1599

页数：4

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