Guest Editorial Computational Imaging for Earth Sciences
The papers in this special section focused on computational imaging for the earth sciences market. 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, exploring, explaining, and exploiting 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 nd 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 solicited 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.