REVISITING EXISTING CLASSIFICATION APPROACHES FOR BUILDING MATERIALS BASED ON HYPERSPECTRAL DATA

Ilehag, R.; Weinmann, M.; Schenk, A.; Keller, S.; Jutzi, B.; Hinz, S.

doi:https://doi.org/10.5194/isprs-archives-XLII-3-W3-65-2017

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Articles | Volume XLII-3/W3

https://doi.org/10.5194/isprs-archives-XLII-3-W3-65-2017

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19 Oct 2017

| 19 Oct 2017

REVISITING EXISTING CLASSIFICATION APPROACHES FOR BUILDING MATERIALS BASED ON HYPERSPECTRAL DATA

R. Ilehag, M. Weinmann, A. Schenk, S. Keller, B. Jutzi, and S. Hinz

R. Ilehag

Institute of Photogrammetry and Remote Sensing, Karlsruhe Institute of Technology (KIT), Englerstr. 7, 76131 Karlsruhe, Germany

M. Weinmann

Institute of Photogrammetry and Remote Sensing, Karlsruhe Institute of Technology (KIT), Englerstr. 7, 76131 Karlsruhe, Germany

A. Schenk

Institute of Photogrammetry and Remote Sensing, Karlsruhe Institute of Technology (KIT), Englerstr. 7, 76131 Karlsruhe, Germany

S. Keller

Institute of Photogrammetry and Remote Sensing, Karlsruhe Institute of Technology (KIT), Englerstr. 7, 76131 Karlsruhe, Germany

B. Jutzi

Institute of Photogrammetry and Remote Sensing, Karlsruhe Institute of Technology (KIT), Englerstr. 7, 76131 Karlsruhe, Germany

S. Hinz

Institute of Photogrammetry and Remote Sensing, Karlsruhe Institute of Technology (KIT), Englerstr. 7, 76131 Karlsruhe, Germany

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URBAN MATERIAL CLASSIFICATION USING SPECTRAL AND TEXTURAL FEATURES RETRIEVED FROM AUTOENCODERS

R. Ilehag, J. Leitloff, M. Weinmann, and A. Schenk

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-1-2020, 25–32, https://doi.org/10.5194/isprs-annals-V-1-2020-25-2020,https://doi.org/10.5194/isprs-annals-V-1-2020-25-2020, 2020

CLASSIFICATION AND REPRESENTATION OF COMMONLY USED ROOFING MATERIAL USING MULTISENSORIAL AERIAL DATA

R. Ilehag, D. Bulatov, P. Helmholz, and D. Belton

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-1, 217–224, https://doi.org/10.5194/isprs-archives-XLII-1-217-2018,https://doi.org/10.5194/isprs-archives-XLII-1-217-2018, 2018

CONCEPT FOR CLASSIFYING FACADE ELEMENTS BASED ON MATERIAL, GEOMETRY AND THERMAL RADIATION USING MULTIMODAL UAV REMOTE SENSING

R. Ilehag, A. Schenk, and S. Hinz

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W6, 145–151, https://doi.org/10.5194/isprs-archives-XLII-2-W6-145-2017,https://doi.org/10.5194/isprs-archives-XLII-2-W6-145-2017, 2017

EXPLOITATION OF DIGITAL SURFACE MODELS GENERATED FROM WORLDVIEW-2 DATA FOR SAR SIMULATION TECHNIQUES

R. Ilehag, S. Auer, and P. d’Angelo

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-1-W1, 55–61, https://doi.org/10.5194/isprs-archives-XLII-1-W1-55-2017,https://doi.org/10.5194/isprs-archives-XLII-1-W1-55-2017, 2017

Novel View Synthesis with Neural Radiance Fields for Industrial Robot Applications

Markus Hillemann, Robert Langendörfer, Max Heiken, Max Mehltretter, Andreas Schenk, Martin Weinmann, Stefan Hinz, Christian Heipke, and Markus Ulrich

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-2-2024, 137–144, https://doi.org/10.5194/isprs-archives-XLVIII-2-2024-137-2024,https://doi.org/10.5194/isprs-archives-XLVIII-2-2024-137-2024, 2024

HoloGS: Instant Depth-based 3D Gaussian Splatting with Microsoft HoloLens 2

Miriam Jäger, Theodor Kapler, Michael Feßenbecker, Felix Birkelbach, Markus Hillemann, and Boris Jutzi

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-2-2024, 159–166, https://doi.org/10.5194/isprs-archives-XLVIII-2-2024-159-2024,https://doi.org/10.5194/isprs-archives-XLVIII-2-2024-159-2024, 2024

Machine Learning Approaches for Vehicle Counting on Bridges Based on Global Ground-Based Radar Data

Matthias Arnold and Sina Keller

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., X-2-2024, 1–8, https://doi.org/10.5194/isprs-annals-X-2-2024-1-2024,https://doi.org/10.5194/isprs-annals-X-2-2024-1-2024, 2024

The Potential of Neural Radiance Fields and 3D Gaussian Splatting for 3D Reconstruction from Aerial Imagery

Dennis Haitz, Max Hermann, Aglaja Solana Roth, Michael Weinmann, and Martin Weinmann

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., X-2-2024, 97–104, https://doi.org/10.5194/isprs-annals-X-2-2024-97-2024,https://doi.org/10.5194/isprs-annals-X-2-2024-97-2024, 2024

3D DENSITY-GRADIENT BASED EDGE DETECTION ON NEURAL RADIANCE FIELDS (NERFS) FOR GEOMETRIC RECONSTRUCTION

M. Jäger and B. Jutzi

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-1-W3-2023, 71–78, https://doi.org/10.5194/isprs-archives-XLVIII-1-W3-2023-71-2023,https://doi.org/10.5194/isprs-archives-XLVIII-1-W3-2023-71-2023, 2023

GEOMETRIC ACCURACY ANALYSIS BETWEEN NEURAL RADIANCE FIELDS (NERFS) AND TERRESTRIAL LASER SCANNING (TLS)

I. Petrovska, M. Jäger, D. Haitz, and B. Jutzi

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-1-W3-2023, 153–159, https://doi.org/10.5194/isprs-archives-XLVIII-1-W3-2023-153-2023,https://doi.org/10.5194/isprs-archives-XLVIII-1-W3-2023-153-2023, 2023

COMBINING HOLOLENS WITH INSTANT-NERFS: ADVANCED REAL-TIME 3D MOBILE MAPPING

D. Haitz, B. Jutzi, M. Ulrich, M. Jäger, and P. Hübner

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-1-W1-2023, 167–174, https://doi.org/10.5194/isprs-archives-XLVIII-1-W1-2023-167-2023,https://doi.org/10.5194/isprs-archives-XLVIII-1-W1-2023-167-2023, 2023

POTENTIAL OF MOBILE MAPPING TO CREATE DIGITAL TWINS OF FORESTS

D. Iwaszczuk, M. Goebel, Y. Du, J. Schmidt, and M. Weinmann

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-1-W1-2023, 199–206, https://doi.org/10.5194/isprs-archives-XLVIII-1-W1-2023-199-2023,https://doi.org/10.5194/isprs-archives-XLVIII-1-W1-2023-199-2023, 2023

A COMPARATIVE NEURAL RADIANCE FIELD (NERF) 3D ANALYSIS OF CAMERA POSES FROM HOLOLENS TRAJECTORIES AND STRUCTURE FROM MOTION

M. Jäger, P. Hübner, D. Haitz, and B. Jutzi

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-1-W1-2023, 207–213, https://doi.org/10.5194/isprs-archives-XLVIII-1-W1-2023-207-2023,https://doi.org/10.5194/isprs-archives-XLVIII-1-W1-2023-207-2023, 2023

MATHEMATICAL AND PHYSICAL APPROACHES TO INFER ABSOLUTE ZENITH WET DELAYS FROM DOUBLE DIFFERENTIAL INTERFEROMETRIC OBSERVATIONS USING ERA5 ATMOSPHERIC REANALYSIS

B. Kamm, A. Schenk, P. Yuan, and S. Hinz

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-M-1-2023, 153–159, https://doi.org/10.5194/isprs-archives-XLVIII-M-1-2023-153-2023,https://doi.org/10.5194/isprs-archives-XLVIII-M-1-2023-153-2023, 2023

Tropospheric water vapor: a comprehensive high-resolution data collection for the transnational Upper Rhine Graben region

Benjamin Fersch, Andreas Wagner, Bettina Kamm, Endrit Shehaj, Andreas Schenk, Peng Yuan, Alain Geiger, Gregor Moeller, Bernhard Heck, Stefan Hinz, Hansjörg Kutterer, and Harald Kunstmann

Earth Syst. Sci. Data, 14, 5287–5307, https://doi.org/10.5194/essd-14-5287-2022,https://doi.org/10.5194/essd-14-5287-2022, 2022

Short summary

In this study, a comprehensive multi-disciplinary dataset for tropospheric water vapor was developed. Geodetic, photogrammetric, and atmospheric modeling and data fusion techniques were used to obtain maps of water vapor in a high spatial and temporal resolution. It could be shown that regional weather simulations for different seasons benefit from assimilating these maps and that the combination of the different observation techniques led to positive synergies.

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PREFACE: TECHNICAL COMMISSION I

S. Hinz, R. Q. Feitosa, M. Weinmann, and B. Jutzi

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B1-2022, 7–7, https://doi.org/10.5194/isprs-archives-XLIII-B1-2022-7-2022,https://doi.org/10.5194/isprs-archives-XLIII-B1-2022-7-2022, 2022

CORROSION DETECTION FOR INDUSTRIAL OBJECTS: FROM MULTI-SENSOR SYSTEM TO 5D FEATURE SPACE

D. Haitz, B. Jutzi, P. Hübner, and M. Ulrich

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B1-2022, 143–150, https://doi.org/10.5194/isprs-archives-XLIII-B1-2022-143-2022,https://doi.org/10.5194/isprs-archives-XLIII-B1-2022-143-2022, 2022

COMPARISON AND EVALUATION OF DIFFERENT APPROACHES FOR EFFICIENT PROCESSING OF LONG GROUND-BASED SAR TIMES SERIES

M. Rebmeister, A. Schenk, and S. Hinz

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2022, 341–348, https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-341-2022,https://doi.org/10.5194/isprs-archives-XLIII-B3-2022-341-2022, 2022

NORMAL CLASSIFICATION OF 3D OCCUPANCY GRIDS FOR VOXEL-BASED INDOOR RECONSTRUCTION FROM POINT CLOUDS

P. Hübner, S. Wursthorn, and M. Weinmann

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-4-2022, 121–128, https://doi.org/10.5194/isprs-annals-V-4-2022-121-2022,https://doi.org/10.5194/isprs-annals-V-4-2022-121-2022, 2022

PREFACE: TECHNICAL COMMISSION I

S. Hinz, R. Q. Feitosa, M. Weinmann, and B. Jutzi

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-1-2022, 7–7, https://doi.org/10.5194/isprs-annals-V-1-2022-7-2022,https://doi.org/10.5194/isprs-annals-V-1-2022-7-2022, 2022

ARM-NMS: SHAPE BASED NON-MAXIMUM SUPPRESSION FOR INSTANCE SEGMENTATION IN LARGE SCALE IMAGERY

A. Michel, W. Gross, S. Hinz, and W. Middelmann

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-2-2022, 291–298, https://doi.org/10.5194/isprs-annals-V-2-2022-291-2022,https://doi.org/10.5194/isprs-annals-V-2-2022-291-2022, 2022

PSDEFOPAT – TOWARDS AUTOMATIC MODEL BASED PSI POST-PROCESSING

M. Evers, A. Thiele, H. Hammer, and S. Hinz

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2022, 107–114, https://doi.org/10.5194/isprs-annals-V-3-2022-107-2022,https://doi.org/10.5194/isprs-annals-V-3-2022-107-2022, 2022

PREFACE: TECHNICAL COMMISSION I

S. Hinz, R. Q. Feitosa, M. Weinmann, and B. Jutzi

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B1-2021, 7–7, https://doi.org/10.5194/isprs-archives-XLIII-B1-2021-7-2021,https://doi.org/10.5194/isprs-archives-XLIII-B1-2021-7-2021, 2021

REAL-TIME DENSE 3D RECONSTRUCTION FROM MONOCULAR VIDEO DATA CAPTURED BY LOW-COST UAVS

M. Hermann, B. Ruf, and M. Weinmann

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2021, 361–368, https://doi.org/10.5194/isprs-archives-XLIII-B2-2021-361-2021,https://doi.org/10.5194/isprs-archives-XLIII-B2-2021-361-2021, 2021

CONCEPT TO ANALYZE THE DISPLACEMENT TIME SERIES OF INDIVIDUAL PERSISTENT SCATTERERS

M. Evers, A. Thiele, H. Hammer, E. Cadario, K. Schulz, and S. Hinz

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2021, 147–154, https://doi.org/10.5194/isprs-archives-XLIII-B3-2021-147-2021,https://doi.org/10.5194/isprs-archives-XLIII-B3-2021-147-2021, 2021

PREFACE: TECHNICAL COMMISSION I

S. Hinz, R. Q. Feitosa, M. Weinmann, and B. Jutzi

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-1-2021, 7–7, https://doi.org/10.5194/isprs-annals-V-1-2021-7-2021,https://doi.org/10.5194/isprs-annals-V-1-2021-7-2021, 2021

CONVOLUTIONAL NEURAL NETWORKS FOR DETECTING BRIDGE CROSSING EVENTS WITH GROUND-BASED INTERFEROMETRIC RADAR DATA

M. Arnold, M. Hoyer, and S. Keller

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-1-2021, 31–38, https://doi.org/10.5194/isprs-annals-V-1-2021-31-2021,https://doi.org/10.5194/isprs-annals-V-1-2021-31-2021, 2021

KNOWLEDGE AND SKILLS RELATED TO ACTIVE OPTICAL SENSORS IN THE BODY OF KNOWLEDGE FOR EARTH OBSERVATION AND GEOINFORMATION (EO4GEO BOK)

C. Dubois, B. Jutzi, M. Olijslagers, C. Pathe, C. Schmullius, M. A. Stelmaszczuk-Górska, D. Vandenbroucke, and M. Weinmann

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-5-2021, 9–16, https://doi.org/10.5194/isprs-annals-V-5-2021-9-2021,https://doi.org/10.5194/isprs-annals-V-5-2021-9-2021, 2021

PREFACE: TECHNICAL COMMISSION I

S. Hinz, R. Q. Feitosa, M. Weinmann, and B. Jutzi

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B1-2020, 7–7, https://doi.org/10.5194/isprs-archives-XLIII-B1-2020-7-2020,https://doi.org/10.5194/isprs-archives-XLIII-B1-2020-7-2020, 2020

INCORPORATING INTERFEROMETRIC COHERENCE INTO LULC CLASSIFICATION OF AIRBORNE POLSAR-IMAGES USING FULLY CONVOLUTIONAL NETWORKS

S. Schmitz, M. Weinmann, and A. Thiele

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B1-2020, 115–122, https://doi.org/10.5194/isprs-archives-XLIII-B1-2020-115-2020,https://doi.org/10.5194/isprs-archives-XLIII-B1-2020-115-2020, 2020

Preface: Technical Commission I

S. Hinz, R. Q. Feitosa, M. Weinmann, and B. Jutzi

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-1-2020, 7–7, https://doi.org/10.5194/isprs-annals-V-1-2020-7-2020,https://doi.org/10.5194/isprs-annals-V-1-2020-7-2020, 2020

URBAN MATERIAL CLASSIFICATION USING SPECTRAL AND TEXTURAL FEATURES RETRIEVED FROM AUTOENCODERS

R. Ilehag, J. Leitloff, M. Weinmann, and A. Schenk

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-1-2020, 25–32, https://doi.org/10.5194/isprs-annals-V-1-2020-25-2020,https://doi.org/10.5194/isprs-annals-V-1-2020-25-2020, 2020

DETECTION AND CLASSIFICATION OF BRIDGE CROSSING EVENTS WITH GROUND-BASED INTERFEROMETRIC RADAR DATA AND MACHINE LEARNING APPROACHES

M. Arnold and S. Keller

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-1-2020, 109–116, https://doi.org/10.5194/isprs-annals-V-1-2020-109-2020,https://doi.org/10.5194/isprs-annals-V-1-2020-109-2020, 2020

3D INDOOR MAPPING WITH THE MICROSOFT HOLOLENS: QUALITATIVE AND QUANTITATIVE EVALUATION BY MEANS OF GEOMETRIC FEATURES

M. Weinmann, M. A. Jäger, S. Wursthorn, B. Jutzi, M. Weinmann, and P. Hübner

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-1-2020, 165–172, https://doi.org/10.5194/isprs-annals-V-1-2020-165-2020,https://doi.org/10.5194/isprs-annals-V-1-2020-165-2020, 2020

SELF-SUPERVISED LEARNING FOR MONOCULAR DEPTH ESTIMATION FROM AERIAL IMAGERY

M. Hermann, B. Ruf, M. Weinmann, and S. Hinz

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-2-2020, 357–364, https://doi.org/10.5194/isprs-annals-V-2-2020-357-2020,https://doi.org/10.5194/isprs-annals-V-2-2020-357-2020, 2020

VOXEL-BASED INDOOR RECONSTRUCTION FROM HOLOLENS TRIANGLE MESHES

P. Hübner, M. Weinmann, and S. Wursthorn

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-4-2020, 79–86, https://doi.org/10.5194/isprs-annals-V-4-2020-79-2020,https://doi.org/10.5194/isprs-annals-V-4-2020-79-2020, 2020

Observing geometry effects on a Global Navigation Satellite System (GNSS)-based water vapor tomography solved by least squares and by compressive sensing

Marion Heublein, Patrick Erik Bradley, and Stefan Hinz

Ann. Geophys., 38, 179–189, https://doi.org/10.5194/angeo-38-179-2020,https://doi.org/10.5194/angeo-38-179-2020, 2020

IMPACT OF DIFFERENT TRAJECTORIES ON EXTRINSIC SELF-CALIBRATION FOR VEHICLE-BASED MOBILE LASER SCANNING SYSTEMS

M. Hillemann, J. Meidow, and B. Jutzi

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W16, 119–125, https://doi.org/10.5194/isprs-archives-XLII-2-W16-119-2019,https://doi.org/10.5194/isprs-archives-XLII-2-W16-119-2019, 2019

IMAGE-TO-IMAGE TRANSLATION FOR ENHANCED FEATURE MATCHING, IMAGE RETRIEVAL AND VISUAL LOCALIZATION

M. S. Mueller, T. Sattler, M. Pollefeys, and B. Jutzi

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2-W7, 111–119, https://doi.org/10.5194/isprs-annals-IV-2-W7-111-2019,https://doi.org/10.5194/isprs-annals-IV-2-W7-111-2019, 2019

EFFICIENT SURFACE-AWARE SEMI-GLOBAL MATCHING WITH MULTI-VIEW PLANE-SWEEP SAMPLING

B. Ruf, T. Pollok, and M. Weinmann

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2-W7, 137–144, https://doi.org/10.5194/isprs-annals-IV-2-W7-137-2019,https://doi.org/10.5194/isprs-annals-IV-2-W7-137-2019, 2019

PREFACE – ISPRS WORKSHOP LASER SCANNING 2019

J. Boehm, M. Rutzinger, B. Yang, M. Weinmann, B. Riveiro, and W. Yao

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W13, 915–917, https://doi.org/10.5194/isprs-archives-XLII-2-W13-915-2019,https://doi.org/10.5194/isprs-archives-XLII-2-W13-915-2019, 2019

PREFACE – ISPRS WORKSHOP HYPERSPECTRAL SENSING MEETS MACHINE LEARNING AND PATTERN ANALYSIS (HYPERMLPA 2019)

M. Weinmann, R. Müller, R. Reulke, E. Honkavaara, D. Tuia, and S. Keller

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W13, 1825–1826, https://doi.org/10.5194/isprs-archives-XLII-2-W13-1825-2019,https://doi.org/10.5194/isprs-archives-XLII-2-W13-1825-2019, 2019

FUSION OF HYPERSPECTRAL, MULTISPECTRAL, COLOR AND 3D POINT CLOUD INFORMATION FOR THE SEMANTIC INTERPRETATION OF URBAN ENVIRONMENTS

M. Weinmann and M. Weinmann

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W13, 1899–1906, https://doi.org/10.5194/isprs-archives-XLII-2-W13-1899-2019,https://doi.org/10.5194/isprs-archives-XLII-2-W13-1899-2019, 2019

AUTOMATIC CO-REGISTRATION OF AERIAL IMAGERY AND UNTEXTURED MODEL DATA UTILIZING AVERAGE SHADING GRADIENTS

S. Schmitz, M. Weinmann, and B. Ruf

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W13, 581–588, https://doi.org/10.5194/isprs-archives-XLII-2-W13-581-2019,https://doi.org/10.5194/isprs-archives-XLII-2-W13-581-2019, 2019

PREFACE – ISPRS WORKSHOP LASER SCANNING 2019

J. Boehm, M. Rutzinger, B. Yang, M. Weinmann, B. Riveiro, and W. Yao

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2-W5, 313–315, https://doi.org/10.5194/isprs-annals-IV-2-W5-313-2019,https://doi.org/10.5194/isprs-annals-IV-2-W5-313-2019, 2019

PREFACE – HYPERSPECTRAL SENSING MEETS MACHINE LEARNING AND PATTERN ANALYSIS (HYPERMLPA 2019)

M. Weinmann, R. Müller, R. Reulke, E. Honkavaara, D. Tuia, and S. Keller

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2-W5, 607–608, https://doi.org/10.5194/isprs-annals-IV-2-W5-607-2019,https://doi.org/10.5194/isprs-annals-IV-2-W5-607-2019, 2019

ESTIMATING CHLOROPHYLL A CONCENTRATIONS OF SEVERAL INLAND WATERS WITH HYPERSPECTRAL DATA AND MACHINE LEARNING MODELS

P. M. Maier and S. Keller

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2-W5, 609–614, https://doi.org/10.5194/isprs-annals-IV-2-W5-609-2019,https://doi.org/10.5194/isprs-annals-IV-2-W5-609-2019, 2019

SOIL TEXTURE CLASSIFICATION WITH 1D CONVOLUTIONAL NEURAL NETWORKS BASED ON HYPERSPECTRAL DATA

F. M. Riese and S. Keller

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2-W5, 615–621, https://doi.org/10.5194/isprs-annals-IV-2-W5-615-2019,https://doi.org/10.5194/isprs-annals-IV-2-W5-615-2019, 2019

PREFACE

B. Jutzi, M. Weinmann, and S. Hinz

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-1, 1–3, https://doi.org/10.5194/isprs-annals-IV-1-1-2018,https://doi.org/10.5194/isprs-annals-IV-1-1-2018, 2018

SEMANTIC SEGMENTATION OF AERIAL IMAGERY VIA MULTI-SCALE SHUFFLING CONVOLUTIONAL NEURAL NETWORKS WITH DEEP SUPERVISION

K. Chen, M. Weinmann, X. Sun, M. Yan, S. Hinz, B. Jutzi, and M. Weinmann

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-1, 29–36, https://doi.org/10.5194/isprs-annals-IV-1-29-2018,https://doi.org/10.5194/isprs-annals-IV-1-29-2018, 2018

INFRARED MEASUREMENTS AND ESTIMATION OF TEMPERATURE IN THE RESTRICTIVE SCOPE OF AN INDUSTRIAL CEMENT PLANT

R. Gabriel, S. Keller, J. Matthes, P. Waibel, H. B. Keller, and S. Hinz

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-1, 53–60, https://doi.org/10.5194/isprs-annals-IV-1-53-2018,https://doi.org/10.5194/isprs-annals-IV-1-53-2018, 2018

DEVELOPING A MACHINE LEARNING FRAMEWORK FOR ESTIMATING SOIL MOISTURE WITH VNIR HYPERSPECTRAL DATA

S. Keller, F. M. Riese, J. Stötzer, P. M. Maier, and S. Hinz

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-1, 101–108, https://doi.org/10.5194/isprs-annals-IV-1-101-2018,https://doi.org/10.5194/isprs-annals-IV-1-101-2018, 2018

FEASIBILITY INVESTIGATION ON SINGLE PHOTON LIDAR BASED WATER SURFACE MAPPING

G. Mandlburger and B. Jutzi

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-1, 109–116, https://doi.org/10.5194/isprs-annals-IV-1-109-2018,https://doi.org/10.5194/isprs-annals-IV-1-109-2018, 2018

CNN-BASED INITIAL LOCALIZATION IMPROVED BY DATA AUGMENTATION

M. S. Mueller, A. Metzger, and B. Jutzi

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-1, 117–124, https://doi.org/10.5194/isprs-annals-IV-1-117-2018,https://doi.org/10.5194/isprs-annals-IV-1-117-2018, 2018

DEEP CROSS-DOMAIN BUILDING EXTRACTION FOR SELECTIVE DEPTH ESTIMATION FROM OBLIQUE AERIAL IMAGERY

B. Ruf, L. Thiel, and M. Weinmann

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-1, 125–132, https://doi.org/10.5194/isprs-annals-IV-1-125-2018,https://doi.org/10.5194/isprs-annals-IV-1-125-2018, 2018

INVESTIGATIONS ON THE POTENTIAL OF HYPERSPECTRAL AND SENTINEL-2 DATA FOR LAND-COVER / LAND-USE CLASSIFICATION

M. Weinmann, P. M. Maier, J. Florath, and U. Weidner

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-1, 155–162, https://doi.org/10.5194/isprs-annals-IV-1-155-2018,https://doi.org/10.5194/isprs-annals-IV-1-155-2018, 2018

PREFACE

B. Jutzi, M. Weinmann, and S. Hinz

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-1, 1–3, https://doi.org/10.5194/isprs-archives-XLII-1-1-2018,https://doi.org/10.5194/isprs-archives-XLII-1-1-2018, 2018

ON THE CATEGORIZATION OF HIGH ACTIVITY OBJECTS USING DIFFERENTIAL ATTRIBUTE PROFILES

M. Boldt, A. Thiele, K. Schulz, and S. Hinz

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-1, 59–64, https://doi.org/10.5194/isprs-archives-XLII-1-59-2018,https://doi.org/10.5194/isprs-archives-XLII-1-59-2018, 2018

MARKER-BASED LOCALIZATION OF THE MICROSOFT HOLOLENS IN BUILDING MODELS

P. Hübner, M. Weinmann, and S. Wursthorn

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-1, 195–202, https://doi.org/10.5194/isprs-archives-XLII-1-195-2018,https://doi.org/10.5194/isprs-archives-XLII-1-195-2018, 2018

CLASSIFICATION AND REPRESENTATION OF COMMONLY USED ROOFING MATERIAL USING MULTISENSORIAL AERIAL DATA

R. Ilehag, D. Bulatov, P. Helmholz, and D. Belton

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-1, 217–224, https://doi.org/10.5194/isprs-archives-XLII-1-217-2018,https://doi.org/10.5194/isprs-archives-XLII-1-217-2018, 2018

COMBINING INDEPENDENT VISUALIZATION AND TRACKING SYSTEMS FOR AUGMENTED REALITY

P. Hübner, M. Weinmann, M. Hillemann, B. Jutzi, and S. Wursthorn

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2, 455–462, https://doi.org/10.5194/isprs-archives-XLII-2-455-2018,https://doi.org/10.5194/isprs-archives-XLII-2-455-2018, 2018

RESIDUAL SHUFFLING CONVOLUTIONAL NEURAL NETWORKS FOR DEEP SEMANTIC IMAGE SEGMENTATION USING MULTI-MODAL DATA

K. Chen, M. Weinmann, X. Gao, M. Yan, S. Hinz, B. Jutzi, and M. Weinmann

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2, 65–72, https://doi.org/10.5194/isprs-annals-IV-2-65-2018,https://doi.org/10.5194/isprs-annals-IV-2-65-2018, 2018

COLLABORATIVE MULTI-SCALE 3D CITY AND INFRASTRUCTURE MODELING AND SIMULATION

M. Breunig, A. Borrmann, E. Rank, S. Hinz, T. Kolbe, M. Schilcher, R.-P. Mundani, J. R. Jubierre, M. Flurl, A. Thomsen, A. Donaubauer, Y. Ji, S. Urban, S. Laun, S. Vilgertshofer, B. Willenborg, M. Menninghaus, H. Steuer, S. Wursthorn, J. Leitloff, M. Al-Doori, and N Mazroobsemnani

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4-W4, 341–352, https://doi.org/10.5194/isprs-archives-XLII-4-W4-341-2017,https://doi.org/10.5194/isprs-archives-XLII-4-W4-341-2017, 2017

USING MULTI-SCALE FEATURES FOR THE 3D SEMANTIC LABELING OF AIRBORNE LASER SCANNING DATA

R. Blomley and M. Weinmann

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2-W4, 43–50, https://doi.org/10.5194/isprs-annals-IV-2-W4-43-2017,https://doi.org/10.5194/isprs-annals-IV-2-W4-43-2017, 2017

PROJECTOR-BASED AUGMENTED REALITY FOR QUALITY INSPECTION OF SCANNED OBJECTS

J. Kern, M. Weinmann, and S. Wursthorn

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2-W4, 83–90, https://doi.org/10.5194/isprs-annals-IV-2-W4-83-2017,https://doi.org/10.5194/isprs-annals-IV-2-W4-83-2017, 2017

DETERMINING PLANE-SWEEP SAMPLING POINTS IN IMAGE SPACE USING THE CROSS-RATIO FOR IMAGE-BASED DEPTH ESTIMATION

B. Ruf, B. Erdnuess, and M. Weinmann

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W6, 325–332, https://doi.org/10.5194/isprs-archives-XLII-2-W6-325-2017,https://doi.org/10.5194/isprs-archives-XLII-2-W6-325-2017, 2017

POINT CLOUD ANALYSIS FOR UAV-BORNE LASER SCANNING WITH HORIZONTALLY AND VERTICALLY ORIENTED LINE SCANNERS – CONCEPT AND FIRST RESULTS

M. Weinmann, M. S. Müller, M. Hillemann, N. Reydel, S. Hinz, and B. Jutzi

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W6, 399–406, https://doi.org/10.5194/isprs-archives-XLII-2-W6-399-2017,https://doi.org/10.5194/isprs-archives-XLII-2-W6-399-2017, 2017

CONCEPT FOR CLASSIFYING FACADE ELEMENTS BASED ON MATERIAL, GEOMETRY AND THERMAL RADIATION USING MULTIMODAL UAV REMOTE SENSING

R. Ilehag, A. Schenk, and S. Hinz

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W6, 145–151, https://doi.org/10.5194/isprs-archives-XLII-2-W6-145-2017,https://doi.org/10.5194/isprs-archives-XLII-2-W6-145-2017, 2017

SQUEEZEPOSENET: IMAGE BASED POSE REGRESSION WITH SMALL CONVOLUTIONAL NEURAL NETWORKS FOR REAL TIME UAS NAVIGATION

M. S. Müller, S. Urban, and B. Jutzi

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2-W3, 49–57, https://doi.org/10.5194/isprs-annals-IV-2-W3-49-2017,https://doi.org/10.5194/isprs-annals-IV-2-W3-49-2017, 2017

INVESTIGATIONS ON THE POTENTIAL OF CONVOLUTIONAL NEURAL NETWORKS FOR VEHICLE CLASSIFICATION BASED ON RGB AND LIDAR DATA

R. Niessner, H. Schilling, and B. Jutzi

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-1-W1, 115–123, https://doi.org/10.5194/isprs-annals-IV-1-W1-115-2017,https://doi.org/10.5194/isprs-annals-IV-1-W1-115-2017, 2017

GEOMETRIC FEATURES AND THEIR RELEVANCE FOR 3D POINT CLOUD CLASSIFICATION

M. Weinmann, B. Jutzi, C. Mallet, and M. Weinmann

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-1-W1, 157–164, https://doi.org/10.5194/isprs-annals-IV-1-W1-157-2017,https://doi.org/10.5194/isprs-annals-IV-1-W1-157-2017, 2017

EXPLOITATION OF DIGITAL SURFACE MODELS GENERATED FROM WORLDVIEW-2 DATA FOR SAR SIMULATION TECHNIQUES

R. Ilehag, S. Auer, and P. d’Angelo

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-1-W1, 55–61, https://doi.org/10.5194/isprs-archives-XLII-1-W1-55-2017,https://doi.org/10.5194/isprs-archives-XLII-1-W1-55-2017, 2017

NEXT-BEST-VIEW METHOD BASED ON CONSECUTIVE EVALUATION OF TOPOLOGICAL RELATIONS

K. O. Dierenbach, M. Weinmann, and B. Jutzi

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B3, 11–19, https://doi.org/10.5194/isprs-archives-XLI-B3-11-2016,https://doi.org/10.5194/isprs-archives-XLI-B3-11-2016, 2016

Water vapor mapping by fusing InSAR and GNSS remote sensing data and atmospheric simulations

F. Alshawaf, B. Fersch, S. Hinz, H. Kunstmann, M. Mayer, and F. J. Meyer

Hydrol. Earth Syst. Sci., 19, 4747–4764, https://doi.org/10.5194/hess-19-4747-2015,https://doi.org/10.5194/hess-19-4747-2015, 2015

Short summary

This work aims at deriving high spatially resolved maps of atmospheric water vapor by the fusion data from Interferometric Synthetic Aperture Radar (InSAR), Global Navigation Satellite Systems (GNSS), and the Weather Research and Forecasting (WRF) model. The data fusion approach exploits the redundant and complementary spatial properties of all data sets to provide more accurate and high-resolution maps of water vapor. The comparison with maps from MERIS shows rms values of less than 1 mm.

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REVISITING EXISTING CLASSIFICATION APPROACHES FOR BUILDING MATERIALS BASED ON HYPERSPECTRAL DATA

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