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Characterizing radiometric attributes of point cloud using a normalized reflective factor derived from small footprint LiDAR waveform
KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.
TU Munich.
University of Nottingham, Malaysia.
China.
Show others and affiliations
2015 (English)In: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, ISSN 1939-1404, E-ISSN 2151-1535, Vol. 8, no 2, 740-749 p., 6918370Article in journal (Refereed) Published
Abstract [en]

This paper presents a reflectance-like coefficient, normalized reflective factor (NRF) to characterize the radiometric attributes of point cloud generated from small footprint light detection and ranging (LiDAR) waveform data. The NRF is defined as a normalized ratio between the energy of emitted laser beam and the peak in return waveform in conjunction with the atmospheric attenuation and observation geometry. Based on the Gaussian parameters of the emitted and return waveforms, NRF is calculated with an empirical atmospheric model and user-defined standard observation geometry. To correct the radiometric measurement of point cloud in multipeak waveform, a semi-physical-based method is adopted to enhance the NRF of point cloud generated from multipeak waveform. Experiments are conducted with small footprint LiDAR waveform data acquired by RIEGL LMS-Q560. A curve-fitting-based approach is applied to decompose LiDAR waveform into three-dimensional (3-D) coordinates of point cloud, and the NRF are calculated using the Gaussian parameters of both emitted and return waveforms. The visualization of the radiometric attributes of point cloud data is carried out over the overlapping areas between different flight strips, it suggests that the NRF over overlapping area is much smooth than the normalized intensity. Quantitative comparison with Hyperion data indicates that the NRF has much higher correlation with surface reflectance than the normalized intensity data. Standard deviations of NRF and the normalized intensity of different land cover patches are analyzed to assess the homogeneity of the radiometric data. It is observed that NRF has less variability than the normalized intensity within the same land cover patches. Point cloud of two sample trees is also selected to assess the performance of the “sub-footprint” effect correction. It is observed that the proposed approach reduced the variability of radiometric attributes over tree canopies with increa- ing NRF values; which means the “sub-footprint” effect is mitigated. In summary, the proposed NRF can serve as a promising indicator to characterize radiometric attribute of LiDAR point cloud.

Place, publisher, year, edition, pages
2015. Vol. 8, no 2, 740-749 p., 6918370
Keyword [en]
Correction, intensity, normalized reflective factor (NRF), small footprint light detection and ranging (LiDAR), waveform
National Category
Remote Sensing
Identifiers
URN: urn:nbn:se:kth:diva-166141DOI: 10.1109/JSTARS.2014.2354014ISI: 000352277100025Scopus ID: 2-s2.0-84923205891OAI: oai:DiVA.org:kth-166141DiVA: diva2:809365
Note

QC 20150504

Available from: 2015-05-02 Created: 2015-05-02 Last updated: 2017-12-04Bibliographically approved

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