TY  - JOUR
A1  - Brell, Maximilian
A1  - Segl, Karl
A1  - Guanter, Luis
A1  - Bookhagen, Bodo
T1  - 3D hyperspectral point cloud generation
BT  - Fusing airborne laser scanning and hyperspectral imaging sensors for improved object-based information extraction
JF  - ISPRS journal of photogrammetry and remote sensing : official publication of the International Society for Photogrammetry and Remote Sensing
N2  - 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.
KW  - Lidar
KW  - Multispectral point cloud
KW  - Laser return intensity
KW  - Unmixing
KW  - Sharpening
KW  - Imaging spectroscopy
KW  - In-flight
KW  - Pixel level
KW  - Sensor fusion
KW  - Data fusion
KW  - Preprocessing
KW  - Point cloud segmentation
KW  - Semantic labeling
Y1  - 2019
U6  - https://doi.org/10.1016/j.isprsjprs.2019.01.022
SN  - 0924-2716
SN  - 1872-8235
VL  - 149
SP  - 200
EP  - 214
PB  - Elsevier
CY  - Amsterdam
ER  - 
TY  - JOUR
A1  - Cucchi, Karma
A1  - Hesse, Falk
A1  - Kawa, Nura
A1  - Wang, Changhong
A1  - Rubin, Yoram
T1  - Ex-situ priors: A Bayesian hierarchical framework for defining informative prior distributions in hydrogeology
JF  - Advances in water resources
N2  - 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.
KW  - Data assimilation
KW  - Data fusion
KW  - Bayesian hierarchical model
KW  - Informative prior
KW  - Databases
Y1  - 2019
U6  - https://doi.org/10.1016/j.advwatres.2019.02.003
SN  - 0309-1708
SN  - 1872-9657
VL  - 126
SP  - 65
EP  - 78
PB  - Elsevier
CY  - Oxford
ER  -