A comparative study of multiscale representations for spatialspectral classification of hyperspectral imagery

Hyperspectral remote sensors acquire data coming from hundreds of narrow bands through the electromagnetic spectrum; this allows the terrestrial and maritime surfaces to be characterized for Earth observation. Hyperspectral image processing requires algorithms that combine spatial and spectral infor...

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Autores:
Torres-Madronero, Maria Constanza
Tipo de recurso:
Article of journal
Fecha de publicación:
2017
Institución:
Universidad Nacional de Colombia
Repositorio:
Universidad Nacional de Colombia
Idioma:
spa
OAI Identifier:
oai:repositorio.unal.edu.co:unal/60414
Acceso en línea:
https://repositorio.unal.edu.co/handle/unal/60414
http://bdigital.unal.edu.co/58746/
Palabra clave:
62 Ingeniería y operaciones afines / Engineering
difusión no lineal
árbol de partición binaria
clasificación
imagen hiperespectral
representación multiescala
percepción remota
nonlinear diffusion
binary partition tree
classification
hyperspectral imagery
multiscale representation
remote sensing
Rights
openAccess
License
Atribución-NoComercial 4.0 Internacional
Description
Summary:Hyperspectral remote sensors acquire data coming from hundreds of narrow bands through the electromagnetic spectrum; this allows the terrestrial and maritime surfaces to be characterized for Earth observation. Hyperspectral image processing requires algorithms that combine spatial and spectral information. One way to take full advantage of spatial-spectral data within hyperspectral imagery is to use multiscale representations. A multiscale representation generates a family of images were fine details are systematically removed. This paper compares two multiscale representation approaches in order to improve the classification of hyperspectral imagery. The first approach is based on nonlinear diffusion, which obtains a multiscale representation by successive filtering. The second is based on binary partition tree, an approach inspired in region growing. The comparison is performed using a real hyperspectral image and a supper vector machine classifier. Both representation approaches allowed the classification of hyperspectral imagery to be improved. However, nonlinear diffusion results surpassed those obtained using binary partition tree.