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.
Chunsun Zhang, Project Leader
Improved building footprint extraction from LiDAR point clouds has been achieved through the development of new algorithms. The new approach performs global and local reasoning and modelling, generating plausible hypotheses and integrating general knowledge of residential buildings to minimize the effect of the irregular distribution of point clouds along the building edges and close gaps in the area where there are insufficient LiDAR observations. The new algorithms have been tested with good results using low and high density point clouds over residential areas. The tool is available for testing and adaptation.
Algorithms for 3D extraction from full-waveform LiDAR data have been refined and a new process chain to process waveforms has been developed to retrieve accurate cross-section information.
Two papers have been submitted to the International Society for Photogrammetry and Remote Sensing (ISPRS) Commission III Symposium - Photogrammetric Computer Vision 2014. Two papers have been selected for journal publication.