October 8 – 13 , 2017, Dagstuhl Seminar 17411

Hyperspectral, Multispectral, and Multimodal (HMM) Imaging: Acquisition, Algorithms, and Applications


Gonzalo R. Arce (University of Delaware, US)
Richard Bamler (DLR – Oberpfaffenhofen, DE)
Shida Beigpour (MPI für Informatik – Saarbrücken, DE)
Jon Yngve Hardeberg (Norwegian Univ. of Science & Technology – Gjøvik, NO)
Andreas Kolb (Universität Siegen, DE)

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In the last couple of decades, Hyperspectral, Multispectral, and Multimodal (HMM) imaging has emerged as an essential tool in various fields of science, medicine, and technology. Compared to integrated broad-band information as, e.g., present in RGB images, HMM imaging strives to acquire a multitude of specific narrow bands of the electro-magnetic spectrum in order to solve specific detection or analysis tasks. HMM research is interested in studying light-matter interaction in a wide range of wavelengths from the high energy radiation down to Terahertz radiation (submillimeter waves). Furthermore, combining spectral data captured using different imaging modalities can unveil additional information of the scene that is not revealed solely by each of the individual imaging modalities.

HMM imaging has a wide range of applications including automotive industry, automatic material sorting, quality control, security, computer vision, medicine, cultural heritage, and biology. Researchers from many different disciplines are involved in HMM imaging and analysis. Each of these disciplines deals with this topic in a different way according to their own methodologies. Furthermore, the scientific literature related to HMM imaging is scattered over various conferences and journals from different disciplines which makes the communication and knowledge transfer very difficult. However, there are common interests and challenges faced by all of these fields, e.g., reducing costs, handling dimensionality, increasing resolution, visualization, and cross-channel information transferring and mapping which needs to be addressed. The various disciplines exploring HMM imaging can thus benefit from an out-of-the-box thinking and a broader vision of the principal concepts and challenges in HMM. This seminar aims to bring researchers from different scientific communities together, uniting them in tackling the challenges of HMM imaging and analysis.

  Creative Commons BY 3.0 DE
  Gonzalo R. Arce, Richard Bamler, Shida Beigpour, Jon Yngve Hardeberg, and Andreas Kolb


  • Computer Graphics / Computer Vision


  • Hyperspectral and Multispectral imaging and analysis
  • Multi-modal sensor Fusion
  • Computer Vision
  • Remote sensing and Geoscience
  • Compressive Sensing

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Books from the participants of the current Seminar 

Book exhibition in the library, ground floor, during the seminar week.


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