Analysis of hyperspectral images using supervised learning techniques

Abstract : The advantage of hyperspectral image is the possibility to access the spectral signature for each pixel having the capability to see the unseen. The classifiers used for hyperspectral images can be deeply studied and analyzed for obtaining an accurate classification to extract the features from the images. In this paper we aim to analyze the hyperspectral images by trying to identify the nature of each element in the field, which is represented by a pixel, using the techniques of supervised learning.
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Submitted on : Tuesday, November 5, 2019 - 9:22:25 PM
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Laura-Bianca Bilius, Stefan-Gheorghe Pentiuc, David Brie, Sébastian Miron. Analysis of hyperspectral images using supervised learning techniques. 23rd International Conference on System Theory, Control and Computing, ICSTCC 2019, Oct 2019, Sinaia, Romania. ⟨10.1109/ICSTCC.2019.8885627⟩. ⟨hal-02350010⟩



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