Probabilistic topographic maps from raw, full-waveform airborne LiDAR data
Abstract
The main goal of the AutoProbaDTM project is to derive new methodologies to measure the topography and terrain characteristics using the latest full-waveform airborne LiDAR technology. It includes algorithmic development, implementation, and validation over a large test area. In the long run, we wish to develop techniques that are scalable and applicable to future satellite missions such as LIST (NASA Decadal Survey), to help perform efficient and accurate large-scale mapping. One of the biggest challenges is to develop fast ways to process huge volumes of raw data without compromising the accuracy and the physical consistency of the result. Over the past decades, significant progress has been made in digital elevation model (DEM) extraction and user interaction has been much reduced, however most algorithms are still supervised. Topographic surveys currently play a central role in sensor calibration and full automation is still an unsolved problem. Moreover, very few existing methods are currently able to propose a quantitative error map with the reconstructed DEM. Traditional validation and quality control only allow to check the discrepancy between the product and a set of reference points, lacking the ability to predict the actual uncertainty related to elevations at chosen locations. We plan to provide fast and automated techniques to derive topographic maps and to compute error maps as well, based on a probabilistic approach to modeling terrains and data acquisition, solving inverse problems and handling uncertainty. Bayesian inference provides a rigorous framework for model reconstruction and error propagation, treating all quantities as random, and combining sources of information optimally. In the future, the uncertainty maps shall help scientists put error bars on quantities derived from the models. In June 2011, 200 km2 of data were acquired (100 GB of binary files, half a billion waveforms) in central Portugal, over an area of geomorphological and ecological interest, using a Riegl LMS-Q680i sensor. We managed to survey 140 km2 at a satisfactory sampling rate, the angular spacing matching the laser beam divergence and the ground spacing nearly equal to the footprint (almost 4 pts/m2 for a 50cm footprint at 1500 m AGL). We believe this is crucial for a correct processing as aliasing artifacts are significantly reduced, compared to common practice where the spacing is larger than the footprint size. A reverse engineering had to be done as the data were delivered in a proprietary, undocumented binary format, so we were able to read the waveforms and the essential timing and look angle parameters. An instrument model was developed to account for the overall impulse response and noise properties. The instrument was operated in the low signal to noise ratio regime to minimize the cost per km2, and the limits of state of the art processing methods are reached, hence the need for algorithmic development to achieve both a higher detection rate and an improved robustness. We will present the latest results from the first stage of the project: a large DEM of the bare ground topogaphy for the entire study area, including an error estimate for each point, the major novelty being the spatial variability of uncertainty.
- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2011
- Bibcode:
- 2011AGUFMEP51E..06J
- Keywords:
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- 0520 COMPUTATIONAL GEOPHYSICS / Data analysis: algorithms and implementation;
- 0540 COMPUTATIONAL GEOPHYSICS / Image processing;
- 1942 INFORMATICS / Machine learning;
- 1986 INFORMATICS / Statistical methods: Inferential