HyperLabelMe : A Web Platform for Benchmarking Remote-Sensing Image Classifiers
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,...
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| Veröffentlicht in: | IEEE geoscience and remote sensing magazine 2017-12, Vol.5 (4), p.79-85 |
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| container_title | IEEE geoscience and remote sensing magazine |
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| creator | Munoz-Mari, Jordi Izquierdo-Verdiguier, Emma Campos-Taberner, Manuel Perez-Suay, Adrian Gomez-Chova, Luis Mateo-Garcia, Gonzalo Ruescas, Ana B. Laparra, Valero Padron, Jose A. Amoros-Lopez, Julia Camps-Valls, Gustau |
| description | 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. |
| doi_str_mv | 10.1109/MGRS.2017.2762476 |
| format | Article |
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| issn | 2473-2397 2168-6831 |
| language | eng |
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| source | IEEE Electronic Library (IEL) |
| subjects | Benchmark testing Classification algorithms Hyperspectral imaging Image classification Web and Internet services |
| title | HyperLabelMe : A Web Platform for Benchmarking Remote-Sensing Image Classifiers |
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