Feature Selection in Land-Cover Classification using EO-learn

author: Klemen Kenda, Artificial Intelligence Laboratory, Jožef Stefan Institute
published: Nov. 14, 2019,   recorded: October 2019,   views: 24


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Applying machine learning to Big Data can be a cumbersome task which requires a lot of computational power and memory. In this paper we present a feature selection technique for land-cover classiffication in earth observation scenario. The technique extends the state-of-the-art feature extractors by pruning the dimensionality of the required feature space and can achieve almost optimal results with 10-fold reduction of the number of features. The approach utilizes a genetic algorithm for generation of optimal feature vector candidates and multi-objective optimization techniques for candidate selection.

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