Estudio Comparativo de Técnicas de Clasificación de Imágenes Hiperespectrales
DOI:
https://doi.org/10.4995/riai.2019.11078Palabras clave:
Inteligencia artificial, aplicaciones satelitales, análisis de escenas, ingeniería informática, aplicaciones informáticasResumen
Las imágenes hiperespectrales constituyen el núcleo de varios programas de observación remota de la Tierra. La cantidad de información que contienen estas imágenes, formadas por cientos de canales espectrales estrechos y casi continuos, resulta de gran utilidad en aplicaciones en las que la caracterización de los materiales observados en la superficie terrestre resulta de gran relevancia. Esto se debe a la posibilidad de caracterizar de forma inequívoca cada material a través de su firma espectral. Algunas de estas aplicaciones son la agricultura de precisión, la planificación de espacios urbanos, o la prevención y seguimiento de desastres naturales. Sin embargo, la gran dimensión de las imágenes hiperespectrales supone un reto en su tratamiento, almacenamiento y procesamiento, debido a la gran variabilidad espectral y la correlación existente en los datos. En la literatura se han desarrollado múltiples algoritmos de análisis de imágenes hiperespectrales. En este artículo revisamos los algoritmos más utilizados para la clasificación de este tipo de imágenes, realizando experimentos con tres imágenes públicas y presentando una comparativa entre los métodos más ampliamente utilizados en este campo.Descargas
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