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Problem-Based Band Selection for hyperspectral images

Abstract : This paper addresses the band selection of a hyperspectral image. Considering a binary classification, we devise a method to choose the more discriminating bands for the separation of the two classes involved, by using a simple algorithm: single-layer neural network. After that, the most discrimi-native bands are selected, and the resulting reduced data set is used in a more powerful classifier, namely, stacked de-noising autoencoder. Besides its simplicity, the advantage of this method is that the selection of features is made by an algorithm similar to the classifier to be used, and not focused only on the separability measures of the data set. Results indicate the decrease of overfitting for the reduced data set, when compared to the full data architecture.
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Contributor : Vincent Fremont <>
Submitted on : Tuesday, January 9, 2018 - 3:00:29 PM
Last modification on : Tuesday, May 28, 2019 - 4:08:20 PM
Long-term archiving on: : Friday, May 4, 2018 - 11:19:25 PM


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  • HAL Id : hal-01678876, version 1



Mateus Habermann, Vincent Frémont, Elcio Shiguemori. Problem-Based Band Selection for hyperspectral images. IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2017), Jul 2017, Fort Worth, United States. pp.1800-1803. ⟨hal-01678876⟩



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