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Article

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

1
Centrum Wiskunde & Informatica, Science Park 123, 1098 XG Amsterdam, The Netherlands
2
Leiden Institute of Advanced Computer Science, Niels Bohrweg 1, 2333 CA Leiden, The Netherlands
3
Mathematical Institute, Utrecht University, Budapestlaan 6, 3584 CD Utrecht, The Netherlands
4
Faculteit Wiskunde en Informatica, Technical University Eindhoven, Groene Loper 5, 5612 AZ Eindhoven, The Netherlands
*
Author to whom correspondence should be addressed.
Received: 30 October 2020 / Revised: 25 November 2020 / Accepted: 26 November 2020 / Published: 2 December 2020
(This article belongs to the Special Issue Advances in Image Feature Extraction and Selection)
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 preserved in the data reduction step. Convolutional neural network (CNN) approaches are capable of learning the specific features relevant to the particular imaging task, but applying them directly to the spectral input data is constrained by the computational efficiency. We propose a novel supervised deep learning approach for combining data reduction and image analysis in an end-to-end architecture. In our approach, the neural network component that performs the reduction is trained such that image features most relevant for the task are preserved in the reduction step. Results for two convolutional neural network architectures and two types of generated datasets show that the proposed Data Reduction CNN (DRCNN) approach can produce more accurate results than existing popular data reduction methods, and can be used in a wide range of problem settings. The integration of knowledge about the task allows for more image compression and higher accuracies compared to standard data reduction methods. View Full-Text
Keywords: hyperspectral imaging; feature extraction; compression; machine learning; deep learning; convolutional neural network; segmentation hyperspectral imaging; feature extraction; compression; machine learning; deep learning; convolutional neural network; segmentation
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MDPI and ACS Style

Zeegers, M.T.; Pelt, D.M.; van Leeuwen, T.; van Liere, R.; Batenburg, K.J. Task-Driven Learned Hyperspectral Data Reduction Using End-to-End Supervised Deep Learning. J. Imaging 2020, 6, 132. https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging6120132

AMA Style

Zeegers MT, Pelt DM, van Leeuwen T, van Liere R, Batenburg KJ. Task-Driven Learned Hyperspectral Data Reduction Using End-to-End Supervised Deep Learning. Journal of Imaging. 2020; 6(12):132. https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging6120132

Chicago/Turabian Style

Zeegers, Mathé T.; Pelt, Daniël M.; van Leeuwen, Tristan; van Liere, Robert; Batenburg, Kees J. 2020. "Task-Driven Learned Hyperspectral Data Reduction Using End-to-End Supervised Deep Learning" J. Imaging 6, no. 12: 132. https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging6120132

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