Is it possible to make pixel-based radar image classification user-friendly?

Author

Pisani, R. ; Riedel, P. ; Gomes, A. ; Mizobe, R. ; Papa, J.

Author_Institution

Geosci. & Exact Sci. Inst., UNESP-Univ. Estadual Paulista, Paulista, Brazil

fYear

2011

fDate

24-29 July 2011

Firstpage

4304

Lastpage

4307

Abstract

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 or- der 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.

Keywords

decision trees; geophysical image processing; image classification; learning (artificial intelligence); radar imaging; remote sensing by radar; image classification effectiveness; image classification efficiency; moist area classification; offline classification procedures; offline training procedures; online classification procedures; online training procedures; optimum-path forest; pattern recognition algorithms; pixel based radar image classification; training set pruning algorithm also; Accuracy; Machine learning; Prototypes; Radar imaging; Training; Vegetation; moist area classification; optimum-path forest; remote sensing;

fLanguage

English

Publisher

ieee

Conference_Titel

Geoscience and Remote Sensing Symposium (IGARSS), 2011 IEEE International

Conference_Location

Vancouver, BC

ISSN

2153-6996

Print_ISBN

978-1-4577-1003-2

Type

conf

DOI

10.1109/IGARSS.2011.6050183

Filename

6050183