Is it possible to make pixel-based radar image classification user-friendly? - Pisani, R. [UNESP]; Riedel, P. [UNESP]; Gomes, A. [UNESP]; Mizobe, R. [UNESP]; Papa, J. [UNESP] (Institute of Electrical and Electronics Engineers (IEEE), 2011)
In this paper we would like to shed light the problem of efficiency and effectiveness of image classification in large datasets. As the amount of data to be processed and further classified has increased in the last years, there is a need for faster and more precise pattern recognition algorithms in order to perform online and offline training and classification procedures. We deal here with the problem of moist area classification in radar image in a fast manner. Experimental results using Optimum-Path Forest and its training set pruning algorithm also provided and discussed. © 2011 IEEE.
Keywords: moist area classification; optimum-path forest; remote sensing; Area classification; Classification procedure; Large datasets; Off-line training; Pattern recognition algorithms; Pruning algorithms; Radar image; Training sets; Algorithms; Geology; Image analysis; Image classification; Pattern recognition; Radar; Remote sensing; Classification (of information)