@InProceedings{CasagrandeMaSaTuHaMo:2017:PrNeNe,
author = "Casagrande, Luan and Machado, Gustavo Mello and Samiappan,
Sathishkumar and Turnage, Gray and Hathcock, Lee and Moorhead,
Robert",
affiliation = "Department of Computer Engineering, Universidade Federal de Santa
Catarina, Ararangua, SC, Brazil; and Department of Computer
Engineering, Universidade Federal de Santa Catarina, Ararangua,
SC, Brazil; and Geosystems Research Institute, Mississippi State
University, Starkville, MS, USA and Geosystems Research Institute,
Mississippi State University, Starkville, MS, USA and Geosystems
Research Institute, Mississippi State University, Starkville, MS,
USA and Geosystems Research Institute, Mississippi State
University, Starkville, MS, USA",
title = "Probabilistic Neural Network and Wavelet Transform for Mapping of
Phragmites australis Using Low Altitude Remote Sensing",
booktitle = "Proceedings...",
year = "2017",
editor = "Torchelsen, Rafael Piccin and Nascimento, Erickson Rangel do and
Panozzo, Daniele and Liu, Zicheng and Farias, Myl{\`e}ne and
Viera, Thales and Sacht, Leonardo and Ferreira, Nivan and Comba,
Jo{\~a}o Luiz Dihl and Hirata, Nina and Schiavon Porto, Marcelo
and Vital, Creto and Pagot, Christian Azambuja and Petronetto,
Fabiano and Clua, Esteban and Cardeal, Fl{\'a}vio",
organization = "Conference on Graphics, Patterns and Images, 30. (SIBGRAPI)",
publisher = "IEEE Computer Society",
address = "Los Alamitos",
keywords = "Probablistic neural networks, Wavelets, Image texture
classification, Wetlands, Phragmites.",
abstract = "Phragmites australis (common reed) commonly found in the coastal
wetlands can rapidly alter the ecology of these systems by
outcompeting native plant species for resources. Identifying and
mapping Phragmites can help resource managers to restore affected
wetlands. In this work, we use probabilistic neural network with
wavelet texture features for mapping regions with Phragmites in
visible spectrum imagery acquired at low altitude with small
unmanned aerial system. Evaluation study was conducted with
imagery acquired in the delta of the Pearl River located in
southeastern Louisiana and southwestern Mississippi, United States
of America. In comparison to state-of-the-art, our approach
presented improvements in several statistical variables such as
overall accuracy and kappa value. Furthermore, we show that the
remaining omission and commission errors with this technique are
generally located along boundaries of patches with Phragmites,
which reduces unnecessary efforts for resource managers while
searching for nonexistent patches.",
conference-location = "Niter{\'o}i, RJ, Brazil",
conference-year = "17-20 Oct. 2017",
doi = "10.1109/SIBGRAPI.2017.42",
url = "http://dx.doi.org/10.1109/SIBGRAPI.2017.42",
language = "en",
ibi = "8JMKD3MGPAW/3PFRTUH",
url = "http://urlib.net/ibi/8JMKD3MGPAW/3PFRTUH",
targetfile = "paper.pdf",
urlaccessdate = "2024, Apr. 28"
}