@misc{HollsteinSeglGuanteretal.2016, author = {Hollstein, Andr{\´e} and Segl, Karl and Guanter, Luis and Brell, Maximilian and Enesco, Marta}, title = {Ready-to-Use methods for the detection of clouds, cirrus, snow, shadow, water and clear sky pixels in Sentinel-2 MSI images}, series = {remote sensing}, journal = {remote sensing}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-407938}, pages = {18}, year = {2016}, abstract = {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.}, language = {en} } @article{BoescheRogassLubitzetal.2015, author = {B{\"o}sche, Nina Kristine and Rogass, Christian and Lubitz, Christin and Brell, Maximilian and Herrmann, Sabrina and Mielke, Christian and Tonn, Sabine and Appelt, Oona and Altenberger, Uwe and Kaufmann, Hermann}, title = {Hyperspectral REE (Rare Earth Element) Mapping of Outcrops-Applications for Neodymium Detection}, series = {Remote sensing}, volume = {7}, journal = {Remote sensing}, number = {5}, publisher = {MDPI}, address = {Basel}, issn = {2072-4292}, doi = {10.3390/rs70505160}, pages = {5160 -- 5186}, year = {2015}, abstract = {In this study, an in situ application for identifying neodymium (Nd) enriched surface materials that uses multitemporal hyperspectral images is presented (HySpex sensor). Because of the narrow shape and shallow absorption depth of the neodymium absorption feature, a method was developed for enhancing and extracting the necessary information for neodymium from image spectra, even under illumination conditions that are not optimal. For this purpose, the two following approaches were developed: (1) reducing noise and analyzing changing illumination conditions by averaging multitemporal image scenes and (2) enhancing the depth of the desired absorption band by deconvolving every image spectrum with a Gaussian curve while the rest of the spectrum remains unchanged (Richardson-Lucy deconvolution). To evaluate these findings, nine field samples from the Fen complex in Norway were analyzed using handheld X-ray fluorescence devices and by conducting detailed laboratory-based geochemical rare earth element determinations. The result is a qualitative outcrop map that highlights zones that are enriched in neodymium. To reduce the influences of non-optimal illumination, particularly at the studied site, a minimum of seven single acquisitions is required. Sharpening the neodymium absorption band allows for robust mapping, even at the outer zones of enrichment. From the geochemical investigations, we found that iron oxides decrease the applicability of the method. However, iron-related absorption bands can be used as secondary indicators for sulfidic ore zones that are mainly enriched with rare earth elements. In summary, we found that hyperspectral spectroscopy is a noninvasive, fast and cost-saving method for determining neodymium at outcrop surfaces.}, language = {en} } @misc{BoescheRogassLubitzetal.2017, author = {B{\"o}sche, Nina Kristine and Rogass, Christian and Lubitz, Christin and Brell, Maximilian and Herrmann, Sabrina and Mielke, Christian and Tonn, Sabine and Appelt, Oona and Altenberger, Uwe and Kaufmann, Hermann}, title = {Hyperspectral REE (Rare Earth Element) mapping of outcrops}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-400171}, pages = {27}, year = {2017}, abstract = {In this study, an in situ application for identifying neodymium (Nd) enriched surface materials that uses multitemporal hyperspectral images is presented (HySpex sensor). Because of the narrow shape and shallow absorption depth of the neodymium absorption feature, a method was developed for enhancing and extracting the necessary information for neodymium from image spectra, even under illumination conditions that are not optimal. For this purpose, the two following approaches were developed: (1) reducing noise and analyzing changing illumination conditions by averaging multitemporal image scenes and (2) enhancing the depth of the desired absorption band by deconvolving every image spectrum with a Gaussian curve while the rest of the spectrum remains unchanged (Richardson-Lucy deconvolution). To evaluate these findings, nine field samples from the Fen complex in Norway were analyzed using handheld X-ray fluorescence devices and by conducting detailed laboratory-based geochemical rare earth element determinations. The result is a qualitative outcrop map that highlights zones that are enriched in neodymium. To reduce the influences of non-optimal illumination, particularly at the studied site, a minimum of seven single acquisitions is required. Sharpening the neodymium absorption band allows for robust mapping, even at the outer zones of enrichment. From the geochemical investigations, we found that iron oxides decrease the applicability of the method. However, iron-related absorption bands can be used as secondary indicators for sulfidic ore zones that are mainly enriched with rare earth elements. In summary, we found that hyperspectral spectroscopy is a noninvasive, fast and cost-saving method for determining neodymium at outcrop surfaces}, language = {en} } @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{BrellSeglGuanteretal.2017, author = {Brell, Maximilian and Segl, Karl and Guanter, Luis and Bookhagen, Bodo}, title = {Hyperspectral and Lidar Intensity Data Fusion: A Framework for the Rigorous Correction of Illumination, Anisotropic Effects, and Cross Calibration}, series = {IEEE transactions on geoscience and remote sensing}, volume = {55}, journal = {IEEE transactions on geoscience and remote sensing}, publisher = {Inst. of Electr. and Electronics Engineers}, address = {Piscataway}, issn = {0196-2892}, doi = {10.1109/TGRS.2017.2654516}, pages = {2799 -- 2810}, year = {2017}, abstract = {The fusion of hyperspectral imaging (HSI) sensor and airborne lidar scanner (ALS) data provides promising potential for applications in environmental sciences. Standard fusion approaches use reflectance information from the HSI and distance measurements from the ALS to increase data dimen-sionality and geometric accuracy. However, the potential for data fusion based on the respective intensity information of the complementary active and passive sensor systems is high and not yet fully exploited. Here, an approach for the rigorous illumination correction of HSI data, based on the radiometric cross-calibrated return intensity information of ALS data, is presented. The cross calibration utilizes a ray tracing-based fusion of both sensor measurements by intersecting their particular beam shapes. The developed method is capable of compensating for the drawbacks of passive HSI systems, such as cast and cloud shadowing effects, illumination changes over time, across track illumination, and partly anisotropy effects. During processing, spatial and temporal differences in illumination patterns are detected and corrected over the entire HSI wavelength domain. The improvement in the classification accuracy of urban and vegetation surfaces demonstrates the benefit and potential of the proposed HSI illumination correction. The presented approach is the first step toward the rigorous in-flight fusion of passive and active system characteristics, enabling new capabilities for a variety of applications.}, language = {en} } @article{BrellRogassSegletal.2016, author = {Brell, Maximilian and Rogass, Christian and Segl, Karl and Bookhagen, Bodo and Guanter, Luis}, title = {Improving Sensor Fusion: A Parametric Method for the Geometric Coalignment of Airborne Hyperspectral and Lidar Data}, series = {IEEE transactions on geoscience and remote sensing}, volume = {54}, journal = {IEEE transactions on geoscience and remote sensing}, publisher = {Inst. of Electr. and Electronics Engineers}, address = {Piscataway}, issn = {0196-2892}, doi = {10.1109/TGRS.2016.2518930}, pages = {3460 -- 3474}, year = {2016}, abstract = {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.}, language = {en} } @phdthesis{Brell2018, author = {Brell, Maximilian}, title = {Physically based fusion of airborne hyperspectral and lidar data}, school = {Universit{\"a}t Potsdam}, pages = {112}, year = {2018}, language = {en} }