Land Cover Classification from Multispectral Data Using Computational Intelligence Tools: A Comparative Study
In: Information; Volume 8; Issue 4; Pages: 147, Jg. 8 (2017-11-15), Heft 4, p 147
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Zugriff:
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.
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Land Cover Classification from Multispectral Data Using Computational Intelligence Tools: A Comparative Study
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Autor/in / Beteiligte Person: | Lukasik, Szymon ; Silva, João M. N. ; Falcão, A. J. ; Mora, André ; Ribeiro, Rita A. ; Fonseca, José ; Santos, Tiago M. A. |
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Zeitschrift: | Information; Volume 8; Issue 4; Pages: 147, Jg. 8 (2017-11-15), Heft 4, p 147 |
Veröffentlichung: | Multidisciplinary Digital Publishing Institute, 2017 |
Medientyp: | unknown |
ISSN: | 2078-2489 (print) |
DOI: | 10.3390/info8040147 |
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