Research Articles

Application of Numenta® Hierarchical Temporal Memory for land-use classification

A.J. Perea, J.E. Meroño, M.J. Aguilera
South African Journal of Science | Vol 105, No 9/10 | a114 | DOI: https://doi.org/10.4102/sajs.v105i9/10.114 | © 2010 A.J. Perea, J.E. Meroño, M.J. Aguilera | This work is licensed under CC Attribution 4.0
Submitted: 20 January 2010 | Published: 20 January 2010

About the author(s)

A.J. Perea,
J.E. Meroño,
M.J. Aguilera,

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Abstract

The aim of this paper is to present the application of memoryprediction theory, implemented in the form of a Hierarchical Temporal Memory (HTM), for land-use classification. Numenta®HTM is a new computing technology that replicates the structure and function of the human neocortex. In this study, a photogram, received by a photogrammetric UltraCamD® sensor of Vexcel, and data on 1 513 plots in Manzanilla (Huelva, Spain) were used to validate the classification, achieving an overall classification accuracy of 90.4%. The HTMapproach appears to hold promise for land-use classification.

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