Detection of pore space in CT soil images using artificial neural networks

Cortina Januchs, María Guadalupe; Quintanilla Domínguez, Joel; Vega Corona, Antonio; Tarquis Alfonso, Ana Maria y Andina de la Fuente, Diego (2010). Detection of pore space in CT soil images using artificial neural networks. "Biogeosciences", v. 7 (n. 4); pp. 6173-6205. ISSN 1726-4170. https://doi.org/10.5194/bgd-7-6173-2010.

Descripción

Título: Detection of pore space in CT soil images using artificial neural networks
Autor/es:
  • Cortina Januchs, María Guadalupe
  • Quintanilla Domínguez, Joel
  • Vega Corona, Antonio
  • Tarquis Alfonso, Ana Maria
  • Andina de la Fuente, Diego
Tipo de Documento: Artículo
Título de Revista/Publicación: Biogeosciences
Fecha: Agosto 2010
Volumen: 7
Materias:
Escuela: E.T.S.I. Agrónomos (UPM) [antigua denominación]
Departamento: Matemática Aplicada a la Ingeniería Agronómica [hasta 2014]
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

Computed Tomography (CT) images provide a non-invasive alternative for observing soil structures, particularly pore space. Pore space in soil data indicates empty or free space in the sense that no material is present there except fluids such as air, water, and gas. Fluid transport depends on where pore spaces are located in the soil, and for this reason, it is important to identify pore zones. The low contrast between soil and pore space in CT images presents a problem with respect to pore quantification. In this paper, we present a methodology that integrates image processing, clustering techniques and artificial neural networks, in order to classify pore space in soil images. Image processing was used for the feature extraction of images. Three clustering algorithms were implemented (K-means, Fuzzy C-means, and Self Organising Maps) to segment images. The objective of clustering process is to find pixel groups of a similar grey level intensity and to organise them into more or less homogeneous groups. The segmented images are used for test a classifier. An Artificial Neural Network is characterised by a great degree of modularity and flexibility, and it is very efficient for large-scale and generic pattern recognition applications. For these reasons, an Artificial Neural Network was used to classify soil images into two classes (pore space and solid soil). Our methodology shows an alternative way to detect solid soil and pore space in CT images. The percentages of correct classifications of pore space of the total number of classifications among the tested images were 97.01%, 96.47% and 96.12%.

Más información

ID de Registro: 6727
Identificador DC: http://oa.upm.es/6727/
Identificador OAI: oai:oa.upm.es:6727
Identificador DOI: 10.5194/bgd-7-6173-2010
URL Oficial: http://www.biogeosciences-discuss.net/7/6173/2010/bgd-7-6173-2010.html
Depositado por: Memoria Investigacion
Depositado el: 27 Abr 2011 08:49
Ultima Modificación: 20 Abr 2016 15:55
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