IEEE Geoscience and Remote Sensing Magazine - December 2017 - 79

HyperLabelMe

©ISTOCKPHOTO.COM/PEOPLEIMAGES

A web platform for benchmarking
remote-sensing image classifiers

JORDI MUÑOZ-MARÍ, EMMA IZQUIERDO-VERDIGUIER, MANUEL CAMPOS-TABERNER,
ADRIÁN PÉREZ-SUAY, LUIS GÓMEZ-CHOVA, GONZALO MATEO-GARCÍA, ANA B. RUESCAS,
VALERO LAPARRA, JOSÉ A. PADRÓN, JULIA AMORÓS-LÓPEZ, AND GUSTAU CAMPS-VALLS

H

yperLabelMe is a web platform that allows the automatic benchmarking of remote-sensing image classifiers. To demonstrate this platform's attributes, we collected
and harmonized a large data set of labeled multispectral
and hyperspectral images with different numbers of classDigital Object Identifier 10.1109/MGRS.2017.2762476
Date of publication: 27 December 2017

DECEMBER 2017

IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE

es, dimensionality, noise sources, and levels. The registered
user can download training data pairs (spectra and land
cover/use labels) and submit the predictions for unseen
testing spectra. The system then evaluates the accuracy and
robustness of the classifier, and it reports different scores
as well as a ranked list of the best methods and users. The
system is modular, scalable, and ever-growing in data sets
and classifier results.
	

0274-6638/17©2017IEEE

79



http://www.ISTOCKPHOTO.COM/PEOPLEIMAGES

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