%0 Conference Proceedings
%4 sid.inpe.br/sibgrapi/2017/08.22.00.52
%2 sid.inpe.br/sibgrapi/2017/08.22.00.52.58
%@doi 10.1109/SIBGRAPI.2017.42
%T Probabilistic Neural Network and Wavelet Transform for Mapping of Phragmites australis Using Low Altitude Remote Sensing
%D 2017
%A Casagrande, Luan,
%A Machado, Gustavo Mello,
%A Samiappan, Sathishkumar,
%A Turnage, Gray,
%A Hathcock, Lee,
%A Moorhead, Robert,
%@affiliation Department of Computer Engineering, Universidade Federal de Santa Catarina, Ararangua, SC, Brazil;
%@affiliation Department of Computer Engineering, Universidade Federal de Santa Catarina, Ararangua, SC, Brazil;
%@affiliation Geosystems Research Institute, Mississippi State University, Starkville, MS, USA
%@affiliation Geosystems Research Institute, Mississippi State University, Starkville, MS, USA
%@affiliation Geosystems Research Institute, Mississippi State University, Starkville, MS, USA
%@affiliation Geosystems Research Institute, Mississippi State University, Starkville, MS, USA
%E Torchelsen, Rafael Piccin,
%E Nascimento, Erickson Rangel do,
%E Panozzo, Daniele,
%E Liu, Zicheng,
%E Farias, Mylène,
%E Viera, Thales,
%E Sacht, Leonardo,
%E Ferreira, Nivan,
%E Comba, João Luiz Dihl,
%E Hirata, Nina,
%E Schiavon Porto, Marcelo,
%E Vital, Creto,
%E Pagot, Christian Azambuja,
%E Petronetto, Fabiano,
%E Clua, Esteban,
%E Cardeal, Flávio,
%B Conference on Graphics, Patterns and Images, 30 (SIBGRAPI)
%C Niterói, RJ, Brazil
%8 17-20 Oct. 2017
%I IEEE Computer Society
%J Los Alamitos
%S Proceedings
%K Probablistic neural networks, Wavelets, Image texture classification, Wetlands, Phragmites.
%X 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.
%@language en
%3 paper.pdf