Remote sensing and hyperspectral data analysis are areas offering wide range of valuable practical applications. However, they generate massive and complex data that is very difficult to be analyzed by a human being. Therefore, methods for efficient data representation and data mining are of high interest to these fields. In this paper we introduce a novel pipeline for feature extraction and classification of hyperspectral images. To obtain a compressed representation we propose to extract a set of statistical-based properties from these images. This allows for embedding feature space into fourteen channels, obtaining a significant dimensionality reduction. These features are used as an input for the ensemble learning based on minimal-distance classifiers. We introduce a novel method for forming ensembles simple one dimensional classifiers. They are constructed independently on a low-dimensional representation - a single classifier for each extracted feature. Then a voting procedure is being used to obtain the final decision. Extensive experiments carried on a number of benchmarks images prove that using proposed feature extraction and ensemble of simple classifiers can offer a significant improvement in terms of classification accuracy when compared to state-of-the-art methods.
CITATION STYLE
Ksieniewicz, P., Krawczyk, B., & Woźniak, M. (2016). Ensemble of one-dimensional classifiers for hyperspectral image analysis. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9714 LNCS, 513–520. https://doi.org/10.1007/978-3-319-40973-3_52
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