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Bernhard Höfle
Researcher at Heidelberg University
Publications - 160
Citations - 4775
Bernhard Höfle is an academic researcher from Heidelberg University. The author has contributed to research in topics: Point cloud & Lidar. The author has an hindex of 34, co-authored 143 publications receiving 3832 citations. Previous affiliations of Bernhard Höfle include University of Osnabrück & Austrian Institute of Technology.
Papers
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Journal ArticleDOI
Correction of laser scanning intensity data: Data and model-driven approaches
Bernhard Höfle,Norbert Pfeifer +1 more
TL;DR: In this paper, two different methods for correcting the laser scanning intensity data for known influences resulting in a value proportional to the reflectance of the scanned surface are presented, data-driven and model-driven correction.
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Beyond 3-D: The New Spectrum of Lidar Applications for Earth and Ecological Sciences
Jan U. H. Eitel,Bernhard Höfle,Lee A. Vierling,Antonio Abellán,Gregory P. Asner,Jeffrey S. Deems,Craig Glennie,Philip C. Joerg,A. L. LeWinter,Troy S. Magney,Gottfried Mandlburger,Douglas C. Morton,Jörg Müller,Jörg Müller,Kerri T. Vierling +14 more
TL;DR: In this paper, the authors review recent advances based on repeat lidar collections and analysis of LRI data to highlight novel applications of lidar remote sensing beyond 3D, and outline the potential and current challenges of time and LRI information from lidar sensors to expand the scope of research applications.
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Multisource and Multitemporal Data Fusion in Remote Sensing: A Comprehensive Review of the State of the Art
Pedram Ghamisi,Behnood Rasti,Naoto Yokoya,Qunming Wang,Bernhard Höfle,Lorenzo Bruzzone,Francesca Bovolo,Mingmin Chi,Katharina Anders,Richard Gloaguen,Peter M. Atkinson,Jon Atli Benediktsson +11 more
TL;DR: An increase in remote sensing and ancillary data sets opens up the possibility of utilizing multimodal data sets in a joint manner to further improve the performance of the processing approaches with respect to applications at hand.
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Water surface mapping from airborne laser scanning using signal intensity and elevation data
TL;DR: In this article, an automatic method for water surface classification and delineation by combining the geometrical and signal intensity information provided by airborne laser scanning (ALS) is presented. But this method is not suitable for water-land boundary segmentation.
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Hyperspectral and LiDAR Data Fusion Using Extinction Profiles and Deep Convolutional Neural Network
TL;DR: A novel framework for the fusion of hyperspectral and light detection and ranging-derived rasterized data using extinction profiles (EPs) and deep learning and results indicate that the proposed approach can achieve accurate classification results compared to other approaches.