@inproceedings{a8d8d55adb0d4302a64b64af0a321808,
title = "Applying a phenological object-based image analysis (phenobia) for agricultural land classification: A study case in the Brazilian Cerrado",
abstract = "Mapping agriculture with high accuracy is important to generate reliable information about crop production. Pixel-based methods still present problems with noise and usually require post-processing approaches to reach satisfactory results. Object-based Image Analysis (OBIA) enable the detection of homogeneous objects in remote sensing images based on spectral similarity. However, traditional OBIA does not consider the multi-temporal characteristics of land cover or land use, such as agriculture. The objective of this study is to evaluate a phenological object-based approach with dense Landsat image time series for mapping agriculture in different level of detail in the Brazilian Cerrado. We derived pixel-wise EVI fitted time series with 8-day temporal resolution and applied multi-resolution segmentation using all image bands to incorporate the influence of space and time. Then we generated phenological metrics and applied OBIA of agricultural lands in Brazil using a hierarchical classification scheme. The overall accuracies for each hierarchical level were around 90\%, and the spatial consistency of the generated maps is promising.",
keywords = "Big data, OBIA, Phenometrics, Time-series mining, ITC-CV, n/a OA procedure",
author = "Bendini, \{Hugo N.\} and Fonseca, \{Leila M.G.\} and Soares, \{Anderson R.\} and Philippe Rufin and Marcel Schwieder and Rodrigues, \{Marcos A.\} and R.V. Maretto and Korting, \{Thales S.\} and Leitao, \{Pedro J.\} and Sanches, \{Ieda D.A.\} and Patrick Hostert",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020, IGARSS 2020 ; Conference date: 26-09-2020 Through 02-10-2020",
year = "2020",
month = sep,
day = "26",
doi = "10.1109/IGARSS39084.2020.9323184",
language = "English",
isbn = "978-1-7281-6375-8",
series = "Proceedings IEEE International Geoscience and Remote Sensing Symposium (IGARSS)",
publisher = "IEEE",
pages = "1078--1081",
booktitle = "2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Proceedings",
address = "United States",
}