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Information 2017, 8(4), 147; doi:10.3390/info8040147

Land Cover Classification from Multispectral Data Using Computational Intelligence Tools: A Comparative Study

1
Computational Intelligence Group of CTS/UNINOVA, FCT, University NOVA of Lisbon, 2820-516 Caparica, Portugal
2
Faculty of Physics and Applied Computer Science, AGH University of Science and Technology, 30-059 Kraków, Poland
3
Systems Research Institute, Polish Academy of Sciences, 01-447 Warsaw, Poland
4
Forest Research Centre, School of Agriculture, University of Lisbon, 1349-017 Lisbon, Portugal
*
Author to whom correspondence should be addressed.
Received: 8 October 2017 / Revised: 7 November 2017 / Accepted: 13 November 2017 / Published: 15 November 2017
(This article belongs to the Special Issue Fuzzy Logic for Image Processing)
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Abstract

This article discusses how computational intelligence techniques are applied to fuse spectral images into a higher level image of land cover distribution for remote sensing, specifically for satellite image classification. We compare a fuzzy-inference method with two other computational intelligence methods, decision trees and neural networks, using a case study of land cover classification from satellite images. Further, an unsupervised approach based on k-means clustering has been also taken into consideration for comparison. The fuzzy-inference method includes training the classifier with a fuzzy-fusion technique and then performing land cover classification using reinforcement aggregation operators. To assess the robustness of the four methods, a comparative study including three years of land cover maps for the district of Mandimba, Niassa province, Mozambique, was undertaken. Our results show that the fuzzy-fusion method performs similarly to decision trees, achieving reliable classifications; neural networks suffer from overfitting; while k-means clustering constitutes a promising technique to identify land cover types from unknown areas. View Full-Text
Keywords: image fusion; aggregation operators; remote sensing; land cover classification image fusion; aggregation operators; remote sensing; land cover classification
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Mora, A.; Santos, T.M.A.; Łukasik, S.; Silva, J.M.N.; Falcão, A.J.; Fonseca, J.M.; Ribeiro, R.A. Land Cover Classification from Multispectral Data Using Computational Intelligence Tools: A Comparative Study. Information 2017, 8, 147.

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