@article{BrellSeglGuanteretal.2019,
  author    = {Brell, Maximilian and Segl, Karl and Guanter, Luis and Bookhagen, Bodo},
  title     = {3D hyperspectral point cloud generation},
  series = {ISPRS journal of photogrammetry and remote sensing : official publication of the International Society for Photogrammetry and Remote Sensing},
  volume    = {149},
  journal   = {ISPRS journal of photogrammetry and remote sensing : official publication of the International Society for Photogrammetry and Remote Sensing},
  publisher = {Elsevier},
  address   = {Amsterdam},
  issn      = {0924-2716},
  doi       = {10.1016/j.isprsjprs.2019.01.022},
  pages     = {200 -- 214},
  year      = {2019},
  abstract  = {Remote Sensing technologies allow to map biophysical, biochemical, and earth surface parameters of the land surface. Of especial interest for various applications in environmental and urban sciences is the combination of spectral and 3D elevation information. However, those two data streams are provided separately by different instruments, namely airborne laser scanner (ALS) for elevation and a hyperspectral imager (HSI) for high spectral resolution data. The fusion of ALS and HSI data can thus lead to a single data entity consistently featuring rich structural and spectral information. In this study, we present the application of fusing the first pulse return information from ALS data at a sub-decimeter spatial resolution with the lower-spatial resolution hyperspectral information available from the HSI into a hyperspectral point cloud (HSPC). During the processing, a plausible hyperspectral spectrum is assigned to every first-return ALS point. We show that the complementary implementation of spectral and 3D information at the point-cloud scale improves object-based classification and information extraction schemes. This improvements have great potential for numerous land cover mapping and environmental applications.},
  language  = {en}
}
@article{CucchiHesseKawaetal.2019,
  author    = {Cucchi, Karma and Hesse, Falk and Kawa, Nura and Wang, Changhong and Rubin, Yoram},
  title     = {Ex-situ priors: A Bayesian hierarchical framework for defining informative prior distributions in hydrogeology},
  series = {Advances in water resources},
  volume    = {126},
  journal   = {Advances in water resources},
  publisher = {Elsevier},
  address   = {Oxford},
  issn      = {0309-1708},
  doi       = {10.1016/j.advwatres.2019.02.003},
  pages     = {65 -- 78},
  year      = {2019},
  abstract  = {Stochastic modeling is a common practice for modeling uncertainty in hydrogeology. In stochastic modeling, aquifer properties are characterized by their probability density functions (PDFs). The Bayesian approach for inverse modeling is often used to assimilate information from field measurements collected at a site into properties' posterior PDFs. This necessitates the definition of a prior PDF, characterizing the knowledge of hydrological properties before undertaking any investigation at the site, and usually coming from previous studies at similar sites. In this paper, we introduce a Bayesian hierarchical algorithm capable of assimilating various information-like point measurements, bounds and moments-into a single, informative PDF that we call ex-situ prior. This informative PDF summarizes the ex-situ information available about a hydrogeological parameter at a site of interest, which can then be used as a prior PDF in future studies at the site. We demonstrate the behavior of the algorithm on several synthetic case studies, compare it to other methods described in the literature, and illustrate the approach by applying it to a public open-access hydrogeological dataset.},
  language  = {en}
}