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A Comparison of Accuracy Measures for Remote Sensing Image Classification: Case Study In An Amazonian Region Using Support Vector Machine
Graziela Balda Scofield, Eliana Pantaleao, Rogerio Galante Negri
Pages - 11 - 21     |    Revised - 31-1-2015     |    Published - 28-2-2015
Volume - 9   Issue - 1    |    Publication Date - January / February 2015  Table of Contents
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KEYWORDS
Image Classification, Accuracy Measures, Category-level, Map-level, Comparison.
ABSTRACT
This work investigated the consistency of both the category-level and the map-level accuracy measures for different scenarios and features using Support Vector Machine. It was verified that the classification scenario and the features adopted have not influenced the accuracy measure consistency and all accuracy measures are highly positively correlated.
CITED BY (1)  
1 Tiwari, L. K., Sinha, S. K., Saran, S., Tolpekin, V. A., & Raju, P. L. (2016). Forest encroachment mapping in Baratang Island, India, using maximum likelihood and support vector machine classifiers. Journal of Applied Remote Sensing, 10(1), 016016-016016.
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Dr. Graziela Balda Scofield
Centro Nacional de Monitoramento e Alertas de Desastres Naturais - Brazil
graziela.scofield@cemaden.gov.br
Dr. Eliana Pantaleao
Universidade Federal de Uberlandia - Brazil
Dr. Rogerio Galante Negri
Sao Paulo State University (UNESP) - Brazil


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