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dc.contributor.authorHarcourt, William David
dc.contributor.authorMacfarlane, David Graham
dc.contributor.authorRobertson, Duncan A.
dc.date.accessioned2024-02-27T17:30:04Z
dc.date.available2024-02-27T17:30:04Z
dc.date.issued2024-01-12
dc.identifier298159685
dc.identifiere93b9bd0-706d-47f7-9d27-6bf91cc69ae2
dc.identifier85182917825
dc.identifier.citationHarcourt , W D , Macfarlane , D G & Robertson , D A 2024 , ' 3D terrain mapping and filtering from coarse resolution data cubes extracted from real-aperture 94 GHz radar ' , IEEE Transactions on Geoscience and Remote Sensing , vol. 62 , 10398270 . https://doi.org/10.1109/TGRS.2024.3353676en
dc.identifier.issn0196-2892
dc.identifier.otherORCID: /0000-0002-4042-2772/work/154531454
dc.identifier.urihttps://hdl.handle.net/10023/29364
dc.descriptionFunding: William D. Harcourt was funded by the Engineering and Physical Sciences Research Council (EPSRC; grant number: EP/R513337/1) and the Scottish Alliance for Geoscience, Environment and Society (SAGES).en
dc.description.abstractAccurate, high-resolution 3-D mapping of environmental terrain is critical in a range of disciplines. In this study, we develop a new technique, called the PCFilt-94 algorithm, to extract 3-D point clouds from coarse-resolution millimeter-wave radar data cubes and quantify their associated uncertainties. A technique to noncoherently average neighboring waveforms surrounding each AVTIS2 range profile was developed to reduce speckles and was found to reduce point cloud uncertainty by 13% at long range and 20% at short range. Furthermore, a Voronoi-based point cloud outlier removal algorithm was implemented, which iteratively removes outliers in a point cloud until the process converges to the removal of 0 points. Taken together, the new processing methodology produces a stable point cloud, which means that: 1) it is repeatable even when using different point cloud extraction and filtering parameter values during preprocessing and 2) is less sensitive to overfiltering through the point cloud processing workflow. Using an optimal number of ground control points (GCPs) for georeferencing, which was determined to be 3 at close ranges (< 1.5 km) and 5 at long ranges (>3 km), point cloud uncertainty was estimated to be approximately 1.5 m at 1.5 km to 3 m at 3 km and followed a Lorentzian distribution. These uncertainties are smaller than those reported for other close-range radar systems used for terrain mapping. The results of this study should be used as a benchmark for future application of millimeter-wave radar systems for 3-D terrain mapping.
dc.format.extent18
dc.format.extent16106595
dc.language.isoeng
dc.relation.ispartofIEEE Transactions on Geoscience and Remote Sensingen
dc.subjectMillimetre-wave radaren
dc.subject3D point cloudsen
dc.subjectWave-form averagingen
dc.subjectPoint cloud filteringen
dc.subjectQC Physicsen
dc.subjectNDASen
dc.subject.lccQCen
dc.title3D terrain mapping and filtering from coarse resolution data cubes extracted from real-aperture 94 GHz radaren
dc.typeJournal articleen
dc.contributor.institutionUniversity of St Andrews. School of Physics and Astronomyen
dc.identifier.doi10.1109/TGRS.2024.3353676
dc.description.statusPeer revieweden


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