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dc.contributor.authorAyma Quirita, Victor Hugo
dc.contributor.authorAyma, V. A.
dc.contributor.authorGutiérrez Cárdenas, Juan Manuel
dc.contributor.otherAyma Quirita, Víctor Hugoes_PE
dc.contributor.otherGutiérrez Cárdenas, Juan Manueles_PE
dc.identifier.citationAyma, V. H., Ayma, V. A., & Gutierrez, J. (2020). Dimensionality reduction via an orthogonal autoencoder approach for hyperspectral image classification. ISPRS International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLIII-B3-2020, 357-362.
dc.descriptionIndexado en Scopus
dc.description.abstractNowadays, the increasing amount of information provided by hyperspectral sensors requires optimal solutions to ease the subsequent analysis of the produced data. A common issue in this matter relates to the hyperspectral data representation for classification tasks. Existing approaches address the data representation problem by performing a dimensionality reduction over the original data. However, mining complementary features that reduce the redundancy from the multiple levels of hyperspectral images remains challenging. Thus, exploiting the representation power of neural networks based techniques becomes an attractive alternative in this matter. In this work, we propose a novel dimensionality reduction implementation for hyperspectral imaging based on autoencoders, ensuring the orthogonality among features to reduce the redundancy in hyperspectral data. The experiments conducted on the Pavia University, the Kennedy Space Center, and Botswana hyperspectral datasets evidence such representation power of our approach, leading to better classification performances compared to traditional hyperspectral dimensionality reduction algorithms.en_EN
dc.publisherThe International Society for Photogrammetry and Remote Sensinges_PE
dc.sourceRepositorio Institucional - Ulimaes_PE
dc.sourceUniversidad de Limaes_PE
dc.subjectImágenes hiperespectraleses_PE
dc.subjectReducción de dimensión (estadísticas)es_PE
dc.subjectRedes neuronales (Informática)es_PE
dc.subjectHyperspectral Imagingen_EN
dc.subjectDimension reduction (statistics)en_EN
dc.subjectNeural networks (Computer science)en_EN
dc.subject.classificationIngeniería de sistemas / Diseño y métodoses_PE
dc.titleDimensionality reduction via an orthogonal autoencoder approach for hyperspectral image classificationen_EN
dc.type.otherArtículo de conferencia en Scopus
ulima.areas.lineasdeinvestigacionProductividad y empleo / Innovación: tecnologías y productoses_PE
dc.description.peer-reviewRevisión por pareses_PE

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