@InProceedings{Rodrigues:2021:AuDeCe,
author = "Rodrigues, Marcos Lima",
affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)}",
title = "Automatic detection of center pivots using circular hough
transform, balanced random forest and land use and land cover
data",
booktitle = "Resumos...",
year = "2021",
editor = "Santos, Rafael Duarte Coelho dos and Queiroz, Gilberto Ribeiro de
and Shiguemori, Elcio Hideiti",
organization = "Workshop dos Cursos de Computa{\c{c}}{\~a}o Aplicada do INPE,
21. (WORCAP)",
publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
address = "S{\~a}o Jos{\'e} dos Campos",
abstract = "Water management is a key field to support life and economic
activity nowadays. The greatly increased mechanization of
agriculture, mainly through center pivot irrigation systems,
represents a big challenge to control this resource. Irrigated
agriculture makes up the large majority of consumptive water use,
therefore it is important to identify and quantify these systems.
Currently, with 8.2×10^6 ha, Brazil is among the 10 largest
countries in irrigation areas in the world. In this study, a
combined Computer Vision and Machine Learning approach is proposed
for the identification of center pivots in remote sensing images.
The methodology is based on Circular Hough Transform (CHT) and
Balanced Random Forest (BRF) classifier using vegetation indices
NDVI/SAVI generated from Landsat 8 images and Land Use and Land
Cover (LULC) data provided by project MapBiomas. The candidate's
circles of pivots identified on images are filtered based on
vegetation behavior and shape characteristics of these areas. Our
approach was able to detect 7358 pivots, reaching 83.86% of Recall
for 52 tiles analyzed overall Brazil compared with mapping done by
the Brazilian National Water and Sanitation Agency (ANA). In some
tiles, the Recall reaches up to 100%. The BRF model trained over
spectral and geometric features allowed identify pivots, where
regions with great amplitude of vegetation indices highlight areas
with agricultural activity to the detriment of areas of native
vegetation, and also characteristics of the shapes from targets
based on their delimitation through the High Pass Filter Sharr.
The good accuracy achieved shows the robustness of the method to
detect pivots on a large spatial and temporal scale.",
conference-location = "S{\~a}o Jos{\'e} dos Campos",
conference-year = "13-17 set. 2021",
language = "pt",
organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
ibi = "8JMKD3MGPDW34P/45U7R38",
url = "http://urlib.net/ibi/8JMKD3MGPDW34P/45U7R38",
targetfile = "Rodrigues_automatic.pdf",
urlaccessdate = "12 maio 2024"
}