Superpixel-based roughness measure for multispectral satellite image segmentation

Ortiz Toro, César Antonio; Gonzalo Martín, Consuelo; García Pedrero, Ángel Mario y Menasalvas Ruiz, Ernestina (2015). Superpixel-based roughness measure for multispectral satellite image segmentation. "Remote sensing", v. 7 (n. 11); pp. 14620-14645. ISSN 2072-4292. https://doi.org/10.3390/rs71114620.

Descripción

Título: Superpixel-based roughness measure for multispectral satellite image segmentation
Autor/es:
  • Ortiz Toro, César Antonio
  • Gonzalo Martín, Consuelo
  • García Pedrero, Ángel Mario
  • Menasalvas Ruiz, Ernestina
Tipo de Documento: Artículo
Título de Revista/Publicación: Remote sensing
Fecha: 2015
Volumen: 7
Materias:
Palabras Clave Informales: Unsupervised segmentation; Histon; Rough-set; Region merging
Escuela: E.T.S. de Ingenieros Informáticos (UPM)
Departamento: Arquitectura y Tecnología de Sistemas Informáticos
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

The new generation of artificial satellites is providing a huge amount of Earth observation images whose exploitation can report invaluable benefits, both economical and environmental. However, only a small fraction of this data volume has been analyzed, mainly due to the large human resources needed for that task. In this sense, the development of unsupervised methodologies for the analysis of these images is a priority. In this work, a new unsupervised segmentation algorithm for satellite images is proposed. This algorithm is based on the rough-set theory, and it is inspired by a previous segmentation algorithm defined in the RGB color domain. The main contributions of the new algorithm are: (i) extending the original algorithm to four spectral bands; (ii) the concept of the superpixel is used in order to define the neighborhood similarity of a pixel adapted to the local characteristics of each image; (iii) and two new region merged strategies are proposed and evaluated in order to establish the final number of regions in the segmented image. The experimental results show that the proposed approach improves the results provided by the original method when both are applied to satellite images with different spectral and spatial resolutions.

Más información

ID de Registro: 40845
Identificador DC: http://oa.upm.es/40845/
Identificador OAI: oai:oa.upm.es:40845
Identificador DOI: 10.3390/rs71114620
URL Oficial: http://www.mdpi.com/2072-4292/7/11/14620
Depositado por: Memoria Investigacion
Depositado el: 26 Oct 2016 15:21
Ultima Modificación: 26 Oct 2016 15:21
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