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

Mathé T. Zeegers, Daniël M. Pelt, T. van Leeuwen, Robert van Liere, K.J. Batenburg

Research output: Contribution to journalArticleAcademicpeer-review

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.
Original languageEnglish
Article number132
Number of pages28
JournalJournal of Digital Imaging
Volume6
Issue number12
DOIs
Publication statusPublished - 2 Dec 2020

Keywords

  • hyperspectral imaging
  • feature extraction
  • compression
  • machine learning
  • deep learning
  • convolutional neural network
  • segmentation

Fingerprint

Dive into the research topics of 'Task-Driven Learned Hyperspectral Data Reduction Using End-to-End Supervised Deep Learning'. Together they form a unique fingerprint.

Cite this