Abstract
Land-cover classification can construct a land-use map to analyze satellite images using machine learning. However, supervised machine learning requires a lot of training data since remote sensing data is of higher resolution that reveals many features. Therefore, this study proposed a method to generate self-training data from a small amount of training data. This method generates self-training, which is regarded as the correct class to consider various times and the surrounding land cover. As a result of self-training conducted using this method, the Kappa coefficient was 0.644 for 12 classification problems with one training data per class.
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Oya, Y., Kanamori, K., Ohwada, H. (2016). Recursive Ensemble Land Cover Classification with Little Training Data and Many Classes. In: Nguyen, N.T., Trawiński, B., Fujita, H., Hong, TP. (eds) Intelligent Information and Database Systems. ACIIDS 2016. Lecture Notes in Computer Science(), vol 9621. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-49381-6_50
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DOI: https://doi.org/10.1007/978-3-662-49381-6_50
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