Task-Driven Learned Hyperspectral Data Reduction Using End-to-End Supervised Deep Learning

Task-Driven Learned Hyperspectral Data Reduction Using End-to-End Supervised Deep Learning

An important challenge in hyperspectral imaging tasks is to cope with the large number of spectral bins. Common spectral data reduction methods do not take prior knowledge about the task into account. Consequently, sparsely occurring features that may be essential for the imaging task may not be pre...

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Bibliographic Details
Main Authors: Mathé T. Zeegers, Daniël M. Pelt, Tristan van Leeuwen, Robert van Liere, Kees Joost Batenburg
Format: Article
Language:English
Published: MDPI AG 2020-12-01
Series:Journal of Imaging
Subjects:
hyperspectral imaging
feature extraction
compression
machine learning
deep learning
convolutional neural network
Online Access:https://www.mdpi.com/2313-433X/6/12/132

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https://www.mdpi.com/2313-433X/6/12/132

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