Full-Waveform filtering

Next generation scanners are integrated with full-waveform data acquisitions properties. Every single return pulse is documented. We can research the vertical conditions of the forest areas. Tree, bush, grass...different data class.


Interesting paper about full-waveform datasets....

INTEGRATION OF FULL-WAVEFORM INFORMATION INTO
THE AIRBORNE LASER SCANNING DATA FILTERING PROCESS
Y. -C. Lin and J. P. Mills

"ABSTRACT
Terrain classification of current discrete airborne laser scanning data requires filtering algorithms based on the spatial relationship between neighbouring three-dimensional points. However, difficulties commonly occur with low vegetation on steep slopes and when abrupt changes take place in the terrain. This paper reports on the integration of additional information from latest generation full-waveform data into a filtering algorithm in order to achieve improved digital terrain model (DTM) creation. Prior to a filtering procedure, each point was given an attribute based on pulse width information. A novel routine was then used to integrate pulse width information into the progressive densification filter developed by Axelsson. The performance was investigated in two areas that were found to be problematic when applying typical filtering algorithms. The derived DTM was found to be up to 0.7 m more accurate than the conventional filtering approach. Moreover, compared to typical filtering algorithms, dense low vegetation points could be removed more effectively. Overall, it is recommended that integrating waveform information can provide a solution for areas where typical filtering algorithms cannot perform well. Full-waveform systems are relatively cost-effective in terms of providing additional information without the need to fuse data from other sensors.

CONCLUSIONS
This study set out to investigate whether information derived from the latest generation full-waveform, small-footprint airborne laser scanning data could improve digital terrain modelling. A novel routine was designed to integrate waveform information into a commonly used filtering algorithm. The preliminary results have demonstrated that integrating pulse width information can provide a solution for areas where conventional filtering algorithms cannot perform well. On the top of an artificial mound, points rejected (Type I error) by a typical filtering algorithm can be correctly included in the developed routine. More low vegetation can also be correctly removed. However, rough or steep terrains with low vegetation cover, as well as forest terrain, still require further investigation and detailed validation. In addition, although identifying vegetation points becomes easier with the help of waveform information, it may be the case that in some densely vegetated areas insufficient “true” ground points exist for high-resolution DTM generation. In such cases, it might still be better to assume that the lowest point within a specified or adaptive window size is a ground point. The performance of existing filtering algorithms depends on the type of landscape (Sithole and Vosselman, 2004). Such approaches may require that users determine which type of landscape is being processed and then specify optimal parameters. As demonstrated in this paper, by using waveform information it is possible to automatically determine the landscape characteristics and then use the optimal parameter set for that specific landscape type. This will improve the automation of filtering procedures since less effort is required by users. Moreover, compared to ALS intensity values, pulse width information is easier to apply to different surveys since neither prior calibration or normalization procedures are required. Using full-waveform ALS data provides valuable physical and geometric information simultaneously. Such systems are relatively cost-effective in terms of providing multiple-information without the need to fuse data from other sensors."