Abstract
Terra and Aqua, two satellites launched by the NASA-centered International Earth Observing System project, house MODIS (moderate resolution imaging spectroradiometer) sensors. Moderate-resolution remote sensing allows the quantifying of land-surface type and extent, which can be used to monitor changes in land cover and land use for extended periods of time. In this article, we propose land-surface classification by applying an ensemble technique based on fault masking among individual classifiers in N-version programming. An N-version programming ensemble of artificial neural networks is created, in which the majority vote result is used to predict land-surface cover from MODIS data. It is shown by experiment that an N-version programming ensemble of neural networks greatly improves the classification error rate of land-cover type.
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Yamaguchi, T., Mackin, K.J., Nunohiro, E. et al. Artificial neural network ensemble-based land-cover classifiers using MODIS data. Artif Life Robotics 13, 570–574 (2009). https://doi.org/10.1007/s10015-008-0615-4
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DOI: https://doi.org/10.1007/s10015-008-0615-4