CALL FOR PAPERS: IEEE Transactions on Computational Imaging: Special Issue on Computational Imaging for Earth Sciences

June 26, 2016

From the core of the earth to the farthest reaches of our planets magnetic fields, the earth sciences are concerned with all aspects of monitoring, exploration, explanation, and exploitation of natural events and resources in the geosphere. Revolutions in computational imaging over the past two decades have had profound implications across this field, bringing new modalities into common use and impacting application domains from weather monitoring and prediction, subsurface sensing, seismic imaging and exploration, to the production of minerals, oil and gas. In all these areas, reliable information extraction by collecting and processing sensor data increasingly hinges on integrated sensor design, system modeling, and efficient computational methods. The scope of such models and methods have more recently integrated statistical pattern recognition and machine learning systems to account for variability that cannot be completely captured by physical models or simulation. Enabling most all of these processing methods are concomitant developments in optimization theory both in Euclidean spaces as well as on more general manifolds.

This special issue seeks to compile relevant contributions from researchers in signal and image processing, inverse problems, machine learning, and related areas as applied to problems of image formation and analysis arising in the context of earth science applications covering the wide range of application domains. Within the broad context of computational imaging for earth sciences, topics of interest include but are not limited to the following:

• Computational image formation and tomographic processing
• Full Waveform Inversion (FWI)
• Inverse scattering methods
• Computational hyperspectral imaging
• Advanced machine learning methods for computational imaging
• Joint inversion / sensor fusion
• Probabilistic modeling including random field and graphical models
• Multilinear/tensor methods
• Sparse representations and compressive sensing
• Computational imaging methods for change and target detection

Prospective authors should visit for information on paper submission.

Manuscripts should be submitted via Manuscript Central at

Important Dates:
• Manuscript submission due: September 30, 2016
• Tentative publication date: May 2017

Guest Editors:
Shuchin Aeron (Lead Editor), Tufts University, USA
Eric L. Miller, Tufts University, USA
Melba Crawford, Purdue University, USA
Alison Malcom, Memorial University, Canada.
Andreas Reigber, German Aerospace Center (DLR), Germany
Jocelyn Chanussot, Engineering School on Energy, CH