Please use this identifier to cite or link to this item:
Type: Artigo
Title: Spatio-temporal vegetation pixel classification by using convolutional networks
Author: Nogueira, Keiller
dos Santos, Jefersson A.
Menini, Nathalia
Silva, Thiago S. F.
Morellato, Leonor Patricia C.
Torres, Ricardo da S.
Abstract: Plant phenology studies rely on long-term monitoring of life cycles of plants. High-resolution unmanned aerial vehicles (UAVs) and near-surface technologies have been used for plant monitoring, demanding the creation of methods capable of locating, and identifying plant species through time and space. However, this is a challenging task given the high volume of data, the constant data missing from temporal dataset, the heterogeneity of temporal profiles, the variety of plant visual patterns, and the unclear definition of individuals' boundaries in plant communities. In this letter, we propose a novel method, suitable for phenological monitoring, based on convolutional networks (ConvNets) to perform spatio-temporal vegetation pixel classification on high-resolution images. We conducted a systematic evaluation using high-resolution vegetation image datasets associated with the Brazilian Cerrado biome. Experimental results show that the proposed approach is effective, overcoming other spatio-temporal pixel-classification strategies
Subject: Aprendizado profundo
Country: Estados Unidos
Editor: Institute of Electrical and Electronics Engineers
Rights: Fechado
Identifier DOI: 10.1109/LGRS.2019.2903194
Date Issue: 2019
Appears in Collections:IC - Artigos e Outros Documentos

Files in This Item:
File Description SizeFormat 
000489756100032.pdf2.21 MBAdobe PDFView/Open

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.