This research uses both airborne imagery and LiDAR point clouds for feature extraction. The first significant deliverable will be a process to metrically combine the imagery and LiDAR point clouds to form accurate 3D images. The combined 3D images provide additional information through each of the data sources. These 3D images will be used to automatically extract features for modelling objects such as buildings and vegetation parameters. Further computational tools are being developed to operate and process multi-sensor datasets.
With digital imaging and laser ranging technology advancing over the last decade, both high-resolution commercial satellite imagery, and imagery and laser scan data captured from digital aerial sensors have provided new data sources for spatial information generation.
These data display enhanced spatial, spectral and temporal resolution, allowing for accurate and reliable detection and characterisation of the changing earth in ever more detail.
There is an ongoing explosion in the amount of spatial data provided by new digital sensors. However the associated production of spatial information products is constrained by the slow, expensive and manually intensive processes of feature extraction for mapping and GIS.
Yuxiang developed a new technique for planar structure detection from LiDAR data based on spectral clustering of straight line segments derived from LiDAR scan lines. Experiments are performed on the LiDAR data for ISPRS benchmark test containing a variety of buildings with complex roof structures and varying sizes.
An alternative approach to segmentation of LiDAR point cloud data was developed by Mohammad for automatic extraction of building roof planes. Mohammad and Yuxiang’s research are different but complementary initiatives.
PL Chunsun and PD Clive visited LPI and GA and presented project progress and achievements. Valuable suggestions were received. We also discussed further collaboration in research.
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Chunsun Zhang, CRCSI Conference 2010
Chunsun Zhang, Project Leader