45632
2016
2016
eng
392
414
26
8
article
MDPI
Basel
1
--
--
--
EnGeoMAP 2.0-Automated Hyperspectral Mineral Identification for the German EnMAP Space Mission
Algorithms for a rapid analysis of hyperspectral data are becoming more and more important with planned next generation spaceborne hyperspectral missions such as the Environmental Mapping and Analysis Program (EnMAP) and the Japanese Hyperspectral Imager Suite (HISUI), together with an ever growing pool of hyperspectral airborne data. The here presented EnGeoMAP 2.0 algorithm is an automated system for material characterization from imaging spectroscopy data, which builds on the theoretical framework of the Tetracorder and MICA (Material Identification and Characterization Algorithm) of the United States Geological Survey and of EnGeoMAP 1.0 from 2013. EnGeoMAP 2.0 includes automated absorption feature extraction, spatio-spectral gradient calculation and mineral anomaly detection. The usage of EnGeoMAP 2.0 is demonstrated at the mineral deposit sites of Rodalquilar (SE-Spain) and Haib River (S-Namibia) using HyMAP and simulated EnMAP data. Results from Hyperion data are presented as supplementary information.
Remote sensing
10.3390/rs8020127
2072-4292
wos2016:2019
127
WOS:000371898800004
Mielke, C (reprint author), GFZ German Res Ctr Geosci, Helmholtz Ctr Potsdam, D-14473 Potsdam, Germany.; Mielke, C (reprint author), Univ Potsdam, Inst Earth & Environm Sci, Karl Liebknecht Str 24-25, D-14476 Potsdam, Germany., Christian.Mielke@gfz-potsdam.de; Christian.Rogass@gfz-potsdam.de; Nina.Boesche@gfz-potsdam.de; Karl.Segl@gfz-potsdam.de; uwe@geo.uni-potsdam.de
importub
2020-03-22T20:09:01+00:00
filename=package.tar
a3652796de037e29689bce10171cdffe
Christian Mielke
Christian Rogass
Nina Kristine Bösche
Karl Segl
Uwe Altenberger
eng
uncontrolled
EnMAP
eng
uncontrolled
Hyperion
eng
uncontrolled
EnGeoMAP 2
eng
uncontrolled
0
eng
uncontrolled
mineral mapping
eng
uncontrolled
imaging spectroscopy
Institut für Geowissenschaften
Referiert
Institut für Erd- und Umweltwissenschaften
Import
45304
2016
2016
eng
3460
3474
15
54
article
Inst. of Electr. and Electronics Engineers
Piscataway
1
--
--
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Improving Sensor Fusion: A Parametric Method for the Geometric Coalignment of Airborne Hyperspectral and Lidar Data
Synergistic applications based on integrated hyperspectral and lidar data are receiving a growing interest from the remote-sensing community. A prerequisite for the optimum sensor fusion of hyperspectral and lidar data is an accurate geometric coalignment. The simple unadjusted integration of lidar elevation and hyperspectral reflectance causes a substantial loss of information and does not exploit the full potential of both sensors. This paper presents a novel approach for the geometric coalignment of hyperspectral and lidar airborne data, based on their respective adopted return intensity information. The complete approach incorporates ray tracing and subpixel procedures in order to overcome grid inherent discretization. It aims at the correction of extrinsic and intrinsic (camera resectioning) parameters of the hyperspectral sensor. In additional to a tie-point-based coregistration, we introduce a ray-tracing-based back projection of the lidar intensities for area-based cost aggregation. The approach consists of three processing steps. First is a coarse automatic tie-point-based boresight alignment. The second step coregisters the hyperspectral data to the lidar intensities. Third is a parametric coalignment refinement with an area-based cost aggregation. This hybrid approach of combining tie-point features and area-based cost aggregation methods for the parametric coregistration of hyperspectral intensity values to their corresponding lidar intensities results in a root-mean-square error of 1/3 pixel. It indicates that a highly integrated and stringent combination of different coalignment methods leads to an improvement of the multisensor coregistration.
IEEE transactions on geoscience and remote sensing
10.1109/TGRS.2016.2518930
0196-2892
1558-0644
wos2016:2019
WOS:000377477100029
Brell, M (reprint author), Helmholtz Ctr Potsdam GFZ German Res Ctr Geosci, D-14473 Potsdam, Germany., brell@gfz-potsdam.de
Helmholtz Centre Potsdam GFZ German Research Centre for Geosciences; program "Zentrales Innovationsprogramm Mittelstand (ZIM)"
importub
2020-03-22T17:25:01+00:00
filename=package.tar
7a885fe30c86b6bbc0f4fd253323844f
Maximilian Brell
Christian Rogass
Karl Segl
Bodo Bookhagen
Luis Guanter
eng
uncontrolled
Airborne laser scanning (ALS)
eng
uncontrolled
coregistration
eng
uncontrolled
direct georeferencing
eng
uncontrolled
imaging spectroscopy
eng
uncontrolled
multisensor
eng
uncontrolled
parametric georeferencing
eng
uncontrolled
preprocessing
eng
uncontrolled
ray tracing
eng
uncontrolled
rigorous geocoding
eng
uncontrolled
sensor alignment
eng
uncontrolled
sensor fusion
Institut für Geowissenschaften
Referiert
Institut für Erd- und Umweltwissenschaften
Import
40793
2016
2018
eng
18
postprint
1
2018-06-28
2018-06-28
--
Ready-to-Use methods for the detection of clouds, cirrus, snow, shadow, water and clear sky pixels in Sentinel-2 MSI images
Classification of clouds, cirrus, snow, shadows and clear sky areas is a crucial step in the pre-processing of optical remote sensing images and is a valuable input for their atmospheric correction. The Multi-Spectral Imager on board the Sentinel-2's of the Copernicus program offers optimized bands for this task and delivers unprecedented amounts of data regarding spatial sampling, global coverage, spectral coverage, and repetition rate. Efficient algorithms are needed to process, or possibly reprocess, those big amounts of data. Techniques based on top-of-atmosphere reflectance spectra for single-pixels without exploitation of external data or spatial context offer the largest potential for parallel data processing and highly optimized processing throughput. Such algorithms can be seen as a baseline for possible trade-offs in processing performance when the application of more sophisticated methods is discussed. We present several ready-to-use classification algorithms which are all based on a publicly available database of manually classified Sentinel-2A images. These algorithms are based on commonly used and newly developed machine learning techniques which drastically reduce the amount of time needed to update the algorithms when new images are added to the database. Several ready-to-use decision trees are presented which allow to correctly label about 91% of the spectra within a validation dataset. While decision trees are simple to implement and easy to understand, they offer only limited classification skill. It improves to 98% when the presented algorithm based on the classical Bayesian method is applied. This method has only recently been used for this task and shows excellent performance concerning classification skill and processing performance. A comparison of the presented algorithms with other commonly used techniques such as random forests, stochastic gradient descent, or support vector machines is also given. Especially random forests and support vector machines show similar classification skill as the classical Bayesian method.
remote sensing
urn:nbn:de:kobv:517-opus4-407938
online registration
MDPI Remote sensing (2016) Vol. 8(8), Art. 666 ; DOI: 10.3390/rs8080666
CC-BY - Namensnennung 4.0 International
André Hollstein
Karl Segl
Luis Guanter
Maximilian Brell
Marta Enesco
Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe
455
eng
uncontrolled
Sentinel-2 MSI
eng
uncontrolled
cloud detection
eng
uncontrolled
snow detection
eng
uncontrolled
cirrus detection
eng
uncontrolled
shadow detection
eng
uncontrolled
Bayesian classification
eng
uncontrolled
machine learning
eng
uncontrolled
decision trees
Ingenieurwissenschaften
open_access
Mathematisch-Naturwissenschaftliche Fakultät
Institut für Geowissenschaften
Referiert
Open Access
Multidisciplinary Digital Publishing Institute (MDPI)
Universität Potsdam
https://publishup.uni-potsdam.de/files/40793/pmnr_455.online.pdf