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Synergistic object-based multi-class feature extraction in urban landscape using airborne LiDAR data

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Abstract

This study addresses the potential of geospatial information extraction by using light detection and ranging (LiDAR) data and aerial optical images in an urban landscape. We have adopted an advanced geographic object-based image analysis (GEOBIA) technique consisting of rule-based procedures relying upon the integration of spectral, textural, and spatial characteristics of aerial imagery and roughness of point cloud of LiDAR to fuse aerial imagery and airborne LiDAR for effective urban geospatial information extraction. This study is focused on the extraction of four tangible geospatial features, e.g., buildings, trees, marine vessels, and cars. LiDAR-derived normalized digital surface model (nDSM) was insufficient in delineating the polygon features because of the sparse point cloud density at the edges of features, which greatly affected the accuracy of extracting the polygon features. Therefore aerial imagery was supplemented in order to enhance the quality of extraction. The final feature extraction accuracy was assessed against manual digitization by visual interpretation, statistical analysis, and confusion matrix. The accuracy of feature extraction was found to be ranging from 90 to 95%. The accuracy of buildings class was improved using intensity image generated from LiDAR data and Hough image along with morphological operations. In a nutshell, this study highlights robust improvements in the geospatial extraction of urban features by merging more than one dataset synergistically.

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Notes

  1. IGARSS Contest: http://www.grss-ieee.org/community/technical-committees/data-fusion/2015-ieee-grss-data-fusion-contest.

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Acknowledgements

The authors would like to thank the IEEE GRSS Data Fusion Technical Committee for organizing the 2015 Data Fusion Contest. We are thankful to Prof. Gabriele Moser for his constant support during the whole period of contest.

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Authors and Affiliations

  1. Polar Remote Sensing Section, Polar Sciences Group, Earth System Science Organization, National Centre for Antarctic and Ocean Research (ESSO-NCAOR), Ministry of Earth Sciences, Govt. of India, Headland Sada, Vasco-Da-Gama, Goa, 403804, India

    Shridhar D. Jawak & Alvarinho J. Luis

  2. National Remote Sensing Centre, Indian Space Research Organization, Balanagar, Hyderabad, Telangana, 500037, India

    Satej N. Panditrao

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  1. Shridhar D. Jawak
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Correspondence to Satej N. Panditrao.

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Jawak, S.D., Panditrao, S.N. & Luis, A.J. Synergistic object-based multi-class feature extraction in urban landscape using airborne LiDAR data. Spat. Inf. Res. 26, 483–496 (2018). https://doi.org/10.1007/s41324-018-0191-1

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