A patch-based method for the evaluation of dense image matching quality

Abstract Airborne laser scanning and photogrammetry are two main techniques to obtain 3D data representing the object surface. Due to the high cost of laser scanning, we want to explore the potential of using point clouds derived by dense image matching (DIM), as effective alternatives to laser scanning data. We present a framework to evaluate point clouds from dense image matching and derived Digital Surface Models (DSM) based on automatically extracted sample patches. Dense matching errors and noise level are evaluated quantitatively at both the local level and whole block level. In order to demonstrate its usability, the proposed framework has been used for several example studies identifying the impact of various factors onto the DIM quality. One example study proves that the overall quality on smooth ground areas improves when oblique images are used in addition. This framework is then used to compare the dense matching quality on three different terrain types. In another application of the framework, a bias between the point cloud and the DSM generated from a photogrammetric workflow is identified. The framework is also used to reveal inhomogeneity in the distribution of the dense matching errors caused by overfitting the bundle network to ground control points.

[1]  Norbert Pfeifer,et al.  Dense Image Matching vs. Airborne Laser Scanning – Comparison of two methods for deriving terrain models , 2016 .

[2]  Michael Höhle,et al.  Accuracy assessment of digital elevation models by means of robust statistical methods , 2009 .

[3]  Jean-Stéphane Bailly,et al.  Comparison of Pleiades and LiDAR Digital Elevation Models for Terraces Detection in Farmlands , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[4]  Rongjun Qin,et al.  An Object-Based Hierarchical Method for Change Detection Using Unmanned Aerial Vehicle Images , 2014, Remote. Sens..

[5]  Fabio Remondino,et al.  State of the art in high density image matching , 2014 .

[6]  Jiaojiao Tian,et al.  Exploring Digital Surface Models from Nine Different Sensors for Forest Monitoring and Change Detection , 2017, Remote. Sens..

[7]  Heiko Hirschmüller,et al.  Stereo Processing by Semiglobal Matching and Mutual Information , 2008, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  Weihua Dong,et al.  Selection of LiDAR geometric features with adaptive neighborhood size for urban land cover classification , 2017, Int. J. Appl. Earth Obs. Geoinformation.

[9]  Michael Ying Yang,et al.  Dense matching quality evaluation - an empirical study , 2017, 2017 Joint Urban Remote Sensing Event (JURSE).

[10]  Norbert Haala,et al.  Dense Multi-Stereo Matching for High Quality Digital Elevation Models , 2012 .

[11]  P. Axelsson.  DEM Generation from Laser Scanner Data Using Adaptive TIN Models , 2000 .

[12]  Juha Hyyppä,et al.  Performance of dense digital surface models based on image matching in the estimation of plot-level forest variables , 2013 .

[13]  C. Fraser,et al.  Quality Assessment Of Digital Surface Models Generated From IKONOS Imagery , 2005 .

[14]  Fabio Menna,et al.  A CRITICAL REVIEW OF AUTOMATED PHOTOGRAMMETRICPROCESSING OF LARGE DATASETS , 2017 .

[15]  Bisheng Yang,et al.  Automatic registration of UAV-borne sequent images and LiDAR data , 2015 .

[16]  T. Pock,et al.  Point Clouds: Lidar versus 3D Vision , 2010 .

[17]  Cem Unsalan,et al.  Damaged building detection in aerial images using shadow Information , 2009, 2009 4th International Conference on Recent Advances in Space Technologies.

[18]  Martina L. Hobi,et al.  Accuracy Assessment of Digital Surface Models Based on WorldView-2 and ADS80 Stereo Remote Sensing Data , 2012, Sensors.

[19]  Fabio Remondino,et al.  OBLIQUE PHOTOGRAMMETRY SUPPORTING 3D URBAN RECONSTRUCTION OF COMPLEX SCENARIOS , 2017 .

[20]  Camillo Ressl,et al.  Digital Photogrammetric Camera Evaluation - Generation of Di- gital Elevation Models , 2010 .

[21]  Stefan Hinz,et al.  Semantic point cloud interpretation based on optimal neighborhoods, relevant features and efficient classifiers , 2015 .

[22]  Karsten Jacobsen,et al.  ORIENTATION OF OBLIQUE AIRBORNE IMAGE SETS – EXPERIENCES FROM THE ISPRS/EUROSDR BENCHMARK ON MULTI-PLATFORM PHOTOGRAMMETRY , 2016 .

[23]  M. Jancosek,et al.  Flexible building primitives for 3D building modeling , 2015 .

[24]  Mathias Rothermel,et al.  POTENTIAL OF DENSE MATCHING FOR THE GENERATION OF HIGH QUALITY DIGITAL ELEVATION MODELS , 2012 .

[25]  Dieter Fritsch,et al.  Complementing TLS Point Clouds by Dense Image Matching , 2013 .

[26]  Charalabos Ioannidis,et al.  LIDAR vs dense image matching point clouds in complex urban scenes , 2016, International Conference on Remote Sensing and Geoinformation of Environment.

[27]  Norbert Pfeifer,et al.  IMPROVED TOPOGRAPHIC MODELS VIA CONCURRENT AIRBORNE LIDAR ANDDENSE IMAGE MATCHING , 2017 .

[28]  Fabio Remondino,et al.  ISPRS benchmark for multi - platform photogrammetry , 2015 .

[29]  George Vosselman,et al.  Analysis of planimetric accuracy of airborne laser scanning surveys , 2008 .

[30]  Gottfried Mandlburger,et al.  Local accuracy measures for digital terrain models , 2006 .

[31]  Emmanuel P. Baltsavias,et al.  A comparison between photogrammetry and laser scanning , 1999 .

[32]  Thomas Blaschke,et al.  A building extraction approach for Airborne Laser Scanner data utilizing the Object Based Image Analysis paradigm , 2016, Int. J. Appl. Earth Obs. Geoinformation.

[33]  Christophe Delacourt,et al.  Assessing the Accuracy of High Resolution Digital Surface Models Computed by PhotoScan® and MicMac® in Sub-Optimal Survey Conditions , 2016, Remote. Sens..

[34]  G. Vosselman.  Point cloud segmentation for urban scene classification , 2013 .

[35]  R. McRoberts,et al.  Estimating and mapping forest structural diversity using airborne laser scanning data , 2015 .

[36]  M. Rothermel,et al.  Benchmarking High Density Image Matching for Oblique Airborne Imagery , 2014 .