Land Cover Classification from Multispectral Data Using Computational Intelligence Tools: A Comparative Study
AbstractThis 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
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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.
Mora A, Santos TMA, Łukasik S, Silva JMN, Falcão AJ, Fonseca JM, Ribeiro RA. Land Cover Classification from Multispectral Data Using Computational Intelligence Tools: A Comparative Study. Information. 2017; 8(4):147.Chicago/Turabian Style
Mora, André; Santos, Tiago M.A.; Łukasik, Szymon; Silva, João M.N.; Falcão, António J.; Fonseca, José M.; Ribeiro, Rita A. 2017. "Land Cover Classification from Multispectral Data Using Computational Intelligence Tools: A Comparative Study." Information 8, no. 4: 147.
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