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Prof. Boris Jutzi
Adjunct Professor, Institute of Photogrammetry and Remote Sensing, Karlsruhe Institute of Technology (KIT), Englerstr. 7, Karlsruhe, Germany

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Research Keywords & Expertise

0 Computer Vision
0 Optical measurement technology
0 Active Sensors
0 Laserscanning
0 Signal & Image Processing

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Journal article
Published: 20 August 2019 in Remote Sensing
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Mobile Mapping is an efficient technology to acquire spatial data of the environment. The spatial data is fundamental for applications in crisis management, civil engineering or autonomous driving. The extrinsic calibration of the Mobile Mapping System is a decisive factor that affects the quality of the spatial data. Many existing extrinsic calibration approaches require the use of artificial targets in a time-consuming calibration procedure. Moreover, they are usually designed for a specific combination of sensors and are, thus, not universally applicable. We introduce a novel extrinsic self-calibration algorithm, which is fully automatic and completely data-driven. The fundamental assumption of the self-calibration is that the calibration parameters are estimated the best when the derived point cloud represents the real physical circumstances the best. The cost function we use to evaluate this is based on geometric features which rely on the 3D structure tensor derived from the local neighborhood of each point. We compare different cost functions based on geometric features and a cost function based on the Rényi quadratic entropy to evaluate the suitability for the self-calibration. Furthermore, we perform tests of the self-calibration on synthetic and two different real datasets. The real datasets differ in terms of the environment, the scale and the utilized sensors. We show that the self-calibration is able to extrinsically calibrate Mobile Mapping Systems with different combinations of mapping and pose estimation sensors such as a 2D laser scanner to a Motion Capture System and a 3D laser scanner to a stereo camera and ORB-SLAM2. For the first dataset, the parameters estimated by our self-calibration lead to a more accurate point cloud than two comparative approaches. For the second dataset, which has been acquired via a vehicle-based mobile mapping, our self-calibration achieves comparable results to a manually refined reference calibration, while it is universally applicable and fully automated.

ACS Style

Markus Hillemann; Martin Weinmann; Markus S. Mueller; Boris Jutzi. Automatic Extrinsic Self-Calibration of Mobile Mapping Systems Based on Geometric 3D Features. Remote Sensing 2019, 11, 1955 .

AMA Style

Markus Hillemann, Martin Weinmann, Markus S. Mueller, Boris Jutzi. Automatic Extrinsic Self-Calibration of Mobile Mapping Systems Based on Geometric 3D Features. Remote Sensing. 2019; 11 (16):1955.

Chicago/Turabian Style

Markus Hillemann; Martin Weinmann; Markus S. Mueller; Boris Jutzi. 2019. "Automatic Extrinsic Self-Calibration of Mobile Mapping Systems Based on Geometric 3D Features." Remote Sensing 11, no. 16: 1955.

Journal article
Published: 10 April 2019 in ISPRS International Journal of Geo-Information
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Single photon sensitive airborne Light Detection And Ranging (LiDAR) enables a higher area performance at the price of an increased outlier rate and a lower ranging accuracy compared to conventional Multi-Photon LiDAR. Single Photon LiDAR, in particular, uses green laser light potentially capable of penetrating clear shallow water. The technology is designed for large-area topographic mapping, which also includes the water surface. While the penetration capabilities of green lasers generally lead to underestimation of the water level heights, we specifically focus on the questions of whether Single Photon LiDAR (i) is less affected in this respect due to the high receiver sensitivity, and (ii) consequently delivers sufficient water surface echoes for precise high-resolution water surface reconstruction. After a review of the underlying sensor technology and the interaction of green laser light with water, we address the topic by comparing the surface responses of actual Single Photon LiDAR and Multi-Photon Topo-Bathymetric LiDAR datasets for selected horizontal water surfaces. The anticipated superiority of Single Photon LiDAR could not be verified in this study. While the mean deviations from a reference water level are less than 5 cm for surface models with a cell size of 10 m, systematic water level underestimation of 5–20 cm was observed for high-resolution Single Photon LiDAR based water surface models with cell sizes of 1–5 m. Theoretical photon counts obtained from simulations based on the laser-radar equation support the experimental data evaluation results and furthermore confirm the feasibility of Single Photon LiDAR based high-resolution water surface mapping when adopting specifically tailored flight mission parameters.

ACS Style

Gottfried Mandlburger; Boris Jutzi. On the Feasibility of Water Surface Mapping with Single Photon LiDAR. ISPRS International Journal of Geo-Information 2019, 8, 188 .

AMA Style

Gottfried Mandlburger, Boris Jutzi. On the Feasibility of Water Surface Mapping with Single Photon LiDAR. ISPRS International Journal of Geo-Information. 2019; 8 (4):188.

Chicago/Turabian Style

Gottfried Mandlburger; Boris Jutzi. 2019. "On the Feasibility of Water Surface Mapping with Single Photon LiDAR." ISPRS International Journal of Geo-Information 8, no. 4: 188.

Conference paper
Published: 01 July 2018 in IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium
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In this paper, we focus on the use of multi-modal data to achieve a semantic segmentation of aerial imagery. Thereby, the multi-modal data is composed of a true orthophoto, the Digital Surface Model (DSM) and further representations derived from these. Taking data of different modalities separately and in combination as input to a Residual Shuffling Convolutional Neural Network (RSCNN), we analyze their value for the classification task given with a benchmark dataset. The derived results reveal an improvement if different types of geometric features extracted from the DSM are used in addition to the true orthophoto.

ACS Style

Kaiqiang Chen; Kun Fu; Xian Sun; Michael Weinmann; Stefan Hinz; Boris Jutzi; Martin Weinmann. Deep Semantic Segmentation of Aerial Imagery Based on Multi-Modal Data. IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium 2018, 6219 -6222.

AMA Style

Kaiqiang Chen, Kun Fu, Xian Sun, Michael Weinmann, Stefan Hinz, Boris Jutzi, Martin Weinmann. Deep Semantic Segmentation of Aerial Imagery Based on Multi-Modal Data. IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium. 2018; ():6219-6222.

Chicago/Turabian Style

Kaiqiang Chen; Kun Fu; Xian Sun; Michael Weinmann; Stefan Hinz; Boris Jutzi; Martin Weinmann. 2018. "Deep Semantic Segmentation of Aerial Imagery Based on Multi-Modal Data." IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium , no. : 6219-6222.

Journal article
Published: 13 February 2018 in Drones
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The navigation of Unmanned Aerial Vehicles (UAVs) nowadays is mostly based on Global Navigation Satellite Systems (GNSSs). Drawbacks of satellite-based navigation are failures caused by occlusions or multi-path interferences. Therefore, alternative methods have been developed in recent years. Visual navigation methods such as Visual Odometry (VO) or visual Simultaneous Localization and Mapping (SLAM) aid global navigation solutions by closing trajectory gaps or performing loop closures. However, if the trajectory estimation is interrupted or not available, a re-localization is mandatory. Furthermore, the latest research has shown promising results on pose regression in 6 Degrees of Freedom (DoF) based on Convolutional Neural Networks (CNNs). Additionally, existing navigation methods can benefit from these networks. In this article, a method for GNSS-free and fast image-based pose regression by utilizing a small Convolutional Neural Network is presented. Therefore, a small CNN (SqueezePoseNet) is utilized, transfer learning is applied and the network is tuned for pose regression. Furthermore, recent drawbacks are overcome by applying data augmentation on a training dataset utilizing simulated images. Experiments with small CNNs show promising results for GNSS-free and fast localization compared to larger networks. By training a CNN with an extended data set including simulated images, the accuracy on pose regression is improved up to 61.7% for position and up to 76.0% for rotation compared to training on a standard not-augmented data set.

ACS Style

Markus S. Mueller; Boris Jutzi. UAS Navigation with SqueezePoseNet—Accuracy Boosting for Pose Regression by Data Augmentation. Drones 2018, 2, 7 .

AMA Style

Markus S. Mueller, Boris Jutzi. UAS Navigation with SqueezePoseNet—Accuracy Boosting for Pose Regression by Data Augmentation. Drones. 2018; 2 (1):7.

Chicago/Turabian Style

Markus S. Mueller; Boris Jutzi. 2018. "UAS Navigation with SqueezePoseNet—Accuracy Boosting for Pose Regression by Data Augmentation." Drones 2, no. 1: 7.

Journal article
Published: 30 May 2017 in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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In recent years, there has been a significant improvement in the detection, identification and classification of objects and images using Convolutional Neural Networks. To study the potential of the Convolutional Neural Network, in this paper three approaches are investigated to train classifiers based on Convolutional Neural Networks. These approaches allow Convolutional Neural Networks to be trained on datasets containing only a few hundred training samples, which results in a successful classification. Two of these approaches are based on the concept of transfer learning. In the first approach features, created by a pretrained Convolutional Neural Network, are used for a classification using a support vector machine. In the second approach a pretrained Convolutional Neural Network gets fine-tuned on a different data set. The third approach includes the design and training for flat Convolutional Neural Networks from the scratch. The evaluation of the proposed approaches is based on a data set provided by the IEEE Geoscience and Remote Sensing Society (GRSS) which contains RGB and LiDAR data of an urban area. In this work it is shown that these Convolutional Neural Networks lead to classification results with high accuracy both on RGB and LiDAR data. Features which are derived by RGB data transferred into LiDAR data by transfer learning lead to better results in classification in contrast to RGB data. Using a neural network which contains fewer layers than common neural networks leads to the best classification results. In this framework, it can furthermore be shown that the practical application of LiDAR images results in a better data basis for classification of vehicles than the use of RGB images.

ACS Style

R. Niessner; H. Schilling; B. Jutzi. INVESTIGATIONS ON THE POTENTIAL OF CONVOLUTIONAL NEURAL NETWORKS FOR VEHICLE CLASSIFICATION BASED ON RGB AND LIDAR DATA. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2017, IV-1/W1, 115 -123.

AMA Style

R. Niessner, H. Schilling, B. Jutzi. INVESTIGATIONS ON THE POTENTIAL OF CONVOLUTIONAL NEURAL NETWORKS FOR VEHICLE CLASSIFICATION BASED ON RGB AND LIDAR DATA. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2017; IV-1/W1 ():115-123.

Chicago/Turabian Style

R. Niessner; H. Schilling; B. Jutzi. 2017. "INVESTIGATIONS ON THE POTENTIAL OF CONVOLUTIONAL NEURAL NETWORKS FOR VEHICLE CLASSIFICATION BASED ON RGB AND LIDAR DATA." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences IV-1/W1, no. : 115-123.

Book chapter
Published: 04 March 2017 in Photogrammetrie und Fernerkundung
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In diesem Kapitel werden die grundlegenden Prinzipien von in Wissenschaft und Praxis häufig eingesetzten aktiven Fernerkundungssensoren vorgestellt. Unter aktiven Sensoren werden Messsysteme verstanden, die nicht auf eine Bestrahlung der zu vermessenden Objekte oder Szenen angewiesen sind, sondern die selbst Signale aussenden und deren Echos empfangen. Insbesondere mit Blick auf LASER- und RADAR-Sensoren werden die wichtigsten, teils alternativen, Prinzipien vorgestellt, wie basierend auf dem ausgesandten und wieder empfangenen Signal eine 2D- bzw. 3D-Koordinate inklusive weiterer Information wie z. B. Helligkeit und Phasenlage abgeleitet werden kann. Neben der Interaktion dieses Signals mit den reflektierenden Oberflächen werden v. a. die Messprinzipien, Abbildungsgeometrien sowie die grundlegenden Bild- bzw. Datencharakteristiken beschrieben.

ACS Style

Boris Jutzi; Franz J. Meyer; Stefan Hinz. Aktive Fernerkundungssensorik – Technologische Grundlagen und Abbildungsgeometrie. Photogrammetrie und Fernerkundung 2017, 65 -103.

AMA Style

Boris Jutzi, Franz J. Meyer, Stefan Hinz. Aktive Fernerkundungssensorik – Technologische Grundlagen und Abbildungsgeometrie. Photogrammetrie und Fernerkundung. 2017; ():65-103.

Chicago/Turabian Style

Boris Jutzi; Franz J. Meyer; Stefan Hinz. 2017. "Aktive Fernerkundungssensorik – Technologische Grundlagen und Abbildungsgeometrie." Photogrammetrie und Fernerkundung , no. : 65-103.

Journal article
Published: 17 January 2017 in Journal of Imaging
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In this article, the Laserscanner Multi-Fisheye Camera Dataset (LaFiDa) for applying benchmarks is presented. A head-mounted multi-fisheye camera system combined with a mobile laserscanner was utilized to capture the benchmark datasets. Besides this, accurate six degrees of freedom (6 DoF) ground truth poses were obtained from a motion capture system with a sampling rate of 360 Hz. Multiple sequences were recorded in an indoor and outdoor environment, comprising different motion characteristics, lighting conditions, and scene dynamics. The provided sequences consist of images from three—by hardware trigger—fully synchronized fisheye cameras combined with a mobile laserscanner on the same platform. In total, six trajectories are provided. Each trajectory also comprises intrinsic and extrinsic calibration parameters and related measurements for all sensors. Furthermore, we generalize the most common toolbox for an extrinsic laserscanner to camera calibration to work with arbitrary central cameras, such as omnidirectional or fisheye projections. The benchmark dataset is available online released under the Creative Commons Attributions Licence (CC-BY 4.0), and it contains raw sensor data and specifications like timestamps, calibration, and evaluation scripts. The provided dataset can be used for multi-fisheye camera and/or laserscanner simultaneous localization and mapping (SLAM).

ACS Style

Steffen Urban; Boris Jutzi. LaFiDa—A Laserscanner Multi-Fisheye Camera Dataset. Journal of Imaging 2017, 3, 5 .

AMA Style

Steffen Urban, Boris Jutzi. LaFiDa—A Laserscanner Multi-Fisheye Camera Dataset. Journal of Imaging. 2017; 3 (1):5.

Chicago/Turabian Style

Steffen Urban; Boris Jutzi. 2017. "LaFiDa—A Laserscanner Multi-Fisheye Camera Dataset." Journal of Imaging 3, no. 1: 5.

Journal article
Published: 01 July 2015 in ISPRS Journal of Photogrammetry and Remote Sensing
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ACS Style

Martin Weinmann; Boris Jutzi; Stefan Hinz; Clément Mallet. Semantic point cloud interpretation based on optimal neighborhoods, relevant features and efficient classifiers. ISPRS Journal of Photogrammetry and Remote Sensing 2015, 105, 286 -304.

AMA Style

Martin Weinmann, Boris Jutzi, Stefan Hinz, Clément Mallet. Semantic point cloud interpretation based on optimal neighborhoods, relevant features and efficient classifiers. ISPRS Journal of Photogrammetry and Remote Sensing. 2015; 105 ():286-304.

Chicago/Turabian Style

Martin Weinmann; Boris Jutzi; Stefan Hinz; Clément Mallet. 2015. "Semantic point cloud interpretation based on optimal neighborhoods, relevant features and efficient classifiers." ISPRS Journal of Photogrammetry and Remote Sensing 105, no. : 286-304.

Book chapter
Published: 01 January 2015 in Handbuch der Geodäsie
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ACS Style

Boris Jutzi; Franz Meyer; Stefan Hinz; Willi Freeden; Reiner Rummel. Handbuch der Geodäsie: Aktive Fernerkundungssensorik – Technologische Grundlagen und Abbildungsgeometrie. Handbuch der Geodäsie 2015, 1 -39.

AMA Style

Boris Jutzi, Franz Meyer, Stefan Hinz, Willi Freeden, Reiner Rummel. Handbuch der Geodäsie: Aktive Fernerkundungssensorik – Technologische Grundlagen und Abbildungsgeometrie. Handbuch der Geodäsie. 2015; ():1-39.

Chicago/Turabian Style

Boris Jutzi; Franz Meyer; Stefan Hinz; Willi Freeden; Reiner Rummel. 2015. "Handbuch der Geodäsie: Aktive Fernerkundungssensorik – Technologische Grundlagen und Abbildungsgeometrie." Handbuch der Geodäsie , no. : 1-39.

Journal article
Published: 01 October 2012 in Photogrammetrie - Fernerkundung - Geoinformation
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ACS Style

Uwe Stilla; Helmut Mayer; Michael Schmitt; Boris Jutzi; Franz Rottensteiner; Wolfgang Kresse. Photogrammetric Image Analysis. Photogrammetrie - Fernerkundung - Geoinformation 2012, 2012, 499 -500.

AMA Style

Uwe Stilla, Helmut Mayer, Michael Schmitt, Boris Jutzi, Franz Rottensteiner, Wolfgang Kresse. Photogrammetric Image Analysis. Photogrammetrie - Fernerkundung - Geoinformation. 2012; 2012 (5):499-500.

Chicago/Turabian Style

Uwe Stilla; Helmut Mayer; Michael Schmitt; Boris Jutzi; Franz Rottensteiner; Wolfgang Kresse. 2012. "Photogrammetric Image Analysis." Photogrammetrie - Fernerkundung - Geoinformation 2012, no. 5: 499-500.

Journal article
Published: 31 July 2012 in Photogrammetrie - Fernerkundung - Geoinformation
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ACS Style

Andreas Ch. Braun; Uwe Weidner; Boris Jutzi; Stefan Hinz. Verknüpfung von Kernfunktionen mit der eins-gegen-eins Kaskade für die Einbindung von Wissen in die SVM Klassifizierung. Photogrammetrie - Fernerkundung - Geoinformation 2012, 2012, 371 -384.

AMA Style

Andreas Ch. Braun, Uwe Weidner, Boris Jutzi, Stefan Hinz. Verknüpfung von Kernfunktionen mit der eins-gegen-eins Kaskade für die Einbindung von Wissen in die SVM Klassifizierung. Photogrammetrie - Fernerkundung - Geoinformation. 2012; 2012 (4):371-384.

Chicago/Turabian Style

Andreas Ch. Braun; Uwe Weidner; Boris Jutzi; Stefan Hinz. 2012. "Verknüpfung von Kernfunktionen mit der eins-gegen-eins Kaskade für die Einbindung von Wissen in die SVM Klassifizierung." Photogrammetrie - Fernerkundung - Geoinformation 2012, no. 4: 371-384.

Journal article
Published: 16 September 2011 in Remote Sensing
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The real and imaginary parts are proposed as an alternative to the usual Polar representation of complex-valued images. It is proven that the transformation from Polar to Cartesian representation contributes to decreased mutual information, and hence to greater distinctiveness. The Complex Scale-Invariant Feature Transform (ℂSIFT) detects distinctive features in complex-valued images. An evaluation method for estimating the uniformity of feature distributions in complex-valued images derived from intensity-range images is proposed. In order to experimentally evaluate the proposed methodology on intensity-range images, three different kinds of active sensing systems were used: Range Imaging, Laser Scanning, and Structured Light Projection devices (PMD CamCube 2.0, Z+F IMAGER 5003, Microsoft Kinect).

ACS Style

Patrick Erik Bradley; Boris Jutzi. Improved Feature Detection in Fused Intensity-Range Images with Complex SIFT (ℂSIFT). Remote Sensing 2011, 3, 2076 -2088.

AMA Style

Patrick Erik Bradley, Boris Jutzi. Improved Feature Detection in Fused Intensity-Range Images with Complex SIFT (ℂSIFT). Remote Sensing. 2011; 3 (9):2076-2088.

Chicago/Turabian Style

Patrick Erik Bradley; Boris Jutzi. 2011. "Improved Feature Detection in Fused Intensity-Range Images with Complex SIFT (ℂSIFT)." Remote Sensing 3, no. 9: 2076-2088.

Proceedings article
Published: 01 July 2010 in 2010 IEEE International Geoscience and Remote Sensing Symposium
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Active remote sensing techniques, like SAR tomography and full-waveform LIDAR, are able to capture the 3D reflectance function at or inside objects. They are therefore of special interest for analyzing forest environments. Research goals are the derivation and characterization of the different physical measurement aspects of data taken over forested areas, as well as establishing mutual relations in such a way that LIDAR data can be used to calibrate and correct 3D density data of SAR tomography. The paper outlines the phenomenology of forested areas in SAR tomograms and full-waveform LIDAR data and sketches a simple mathematical methodology for linking SAR and LIDAR reflection density profiles.

ACS Style

Boris Jutzi; Antje Thiele; Franz Meyer; Stefan Hinz. Relations between SAR tomography and full-waveform LIDAR for structural analysis of forested areas. 2010 IEEE International Geoscience and Remote Sensing Symposium 2010, 3267 -3270.

AMA Style

Boris Jutzi, Antje Thiele, Franz Meyer, Stefan Hinz. Relations between SAR tomography and full-waveform LIDAR for structural analysis of forested areas. 2010 IEEE International Geoscience and Remote Sensing Symposium. 2010; ():3267-3270.

Chicago/Turabian Style

Boris Jutzi; Antje Thiele; Franz Meyer; Stefan Hinz. 2010. "Relations between SAR tomography and full-waveform LIDAR for structural analysis of forested areas." 2010 IEEE International Geoscience and Remote Sensing Symposium , no. : 3267-3270.

Book chapter
Published: 10 November 2008 in Topographic Laser Ranging and Scanning
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ACS Style

Uwe Stilla; Boris Jutzi. Waveform Analysis for Small-Footprint Pulsed Laser Systems. Topographic Laser Ranging and Scanning 2008, 215 -234.

AMA Style

Uwe Stilla, Boris Jutzi. Waveform Analysis for Small-Footprint Pulsed Laser Systems. Topographic Laser Ranging and Scanning. 2008; ():215-234.

Chicago/Turabian Style

Uwe Stilla; Boris Jutzi. 2008. "Waveform Analysis for Small-Footprint Pulsed Laser Systems." Topographic Laser Ranging and Scanning , no. : 215-234.