Abstract
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.
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Acknowledgment
This was supported in part by the statutory funds of Department of Systems and Computer Networks, Wrocław University of Technology and by the Polish National Science Center under the grant no. DEC-2013/09/B/ST6/02264.
All experiments were carried out using computer equipment sponsored by EC under FP7, Coordination and Support Action, Grant Agreement Number 316097, ENGINE - European Research Centre of Network Intelligence for Innovation Enhancement (http://engine.pwr.wroc.pl/).
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Ksieniewicz, P., Krawczyk, B., Woźniak, M. (2016). Ensemble of One-Dimensional Classifiers for Hyperspectral Image Analysis. In: Tan, Y., Shi, Y. (eds) Data Mining and Big Data. DMBD 2016. Lecture Notes in Computer Science(), vol 9714. Springer, Cham. https://doi.org/10.1007/978-3-319-40973-3_52
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DOI: https://doi.org/10.1007/978-3-319-40973-3_52
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