HyperLabelMe: A Web Platform for Benchmarking Remote-Sensing Image Classifiers

Jordi Munoz-Mari, E. Izquierdo-Verdiguier, Manuel Campos-Taberner, Adrian Perez-Suay, Luis Gomez-Chova, Gonzalo Mateo-Garcia, Ana B. Ruescas, Valero Laparra, Jose A. Padron, Julia Amoros-Lopez, Gustau Camps-Valls

Research output: Contribution to journalArticleAcademicpeer-review

8 Citations (Scopus)
2 Downloads (Pure)


HyperLabelMe 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 classes, 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.

Original languageEnglish
Pages (from-to)79-85
Number of pages7
JournalIEEE geoscience and remote sensing magazine
Issue number4
Publication statusPublished - 1 Dec 2017


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