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

Journal of Imaging, vol. 6, pp. 132

Abstract

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.

Authors 5

1. Mathé T. Zeegers corresponding

   Centrum Wiskunde & Informatica

   Affiliation as printed

   Centrum Wiskunde & Informatica, Science Park 123, 1098 XG Amsterdam, The Netherlands

2. Daniël M. Pelt Aachen Leiden Institute of Advanced Computer Science

   Leiden University · Centrum Wiskunde & Informatica

   Affiliation as printed

   Centrum Wiskunde & Informatica, Science Park 123, 1098 XG Amsterdam, The Netherlands

   Leiden Institute of Advanced Computer Science, Niels Bohrweg 1, 2333 CA Leiden, The Netherlands

3. Tristan van Leeuwen

   Centrum Wiskunde & Informatica · Utrecht University

   Affiliation as printed

   Centrum Wiskunde & Informatica, Science Park 123, 1098 XG Amsterdam, The Netherlands

   Mathematical Institute, Utrecht University, Budapestlaan 6, 3584 CD Utrecht, The Netherlands

4. Robert van Liere

   Centrum Wiskunde & Informatica · Eindhoven University of Technology

   Affiliation as printed

   Centrum Wiskunde & Informatica, Science Park 123, 1098 XG Amsterdam, The Netherlands

   Faculteit Wiskunde en Informatica, Technical University Eindhoven, Groene Loper 5, 5612 AZ Eindhoven, The Netherlands

5. Kees Joost Batenburg Aachen Leiden Institute of Advanced Computer Science

   Leiden University · Centrum Wiskunde & Informatica

   Affiliation as printed

   Centrum Wiskunde & Informatica, Science Park 123, 1098 XG Amsterdam, The Netherlands

   Leiden Institute of Advanced Computer Science, Niels Bohrweg 1, 2333 CA Leiden, The Netherlands