What is DASOS?
The aim of this software is to enhance the visualisations and classifications of forested areas using coincident full-waveform (fw) LiDAR data and hyperspectral images. It uses either full-waveform LiDAR only or both datasets to generate metrics understandable for foresters and 3D virtual models.
Influenced by Persson et al (2005), voxelisation is an integral part of DASOS. The intensity profile of each full-waveform pulse is accumulated into a voxel array, building up a 3D density volume. The correlation between multiple pulses into a voxel representation produces a more accurate representation, which confers greater noise resistance and it further opens up possibilities of vertical interpretation of the data. The 3D density volume is then aligned with the hyperspectral images using a 2D grid similar to Warren et al (2014) and both datasets are used in visualisations and classifications.
There are three main functionalities of DASOS:
- the generation of 2D metrics aligned with hyperspectral Images
- construction of 3D polygonal meshes and
- the characterisation of objects using feature vectors.
Aligned Metrics
From FW LiDAR (LAS1.3 or Pulswaves file formats):
- Non-Empty Voxels
- Density
- Thickness
- First Patch
- Last Patch
- Lowest Return
- Average Height Difference (works as an edge detection algorithm)
- AGC intensity
A visual explanation of the available full-waveform LiDAR metrics is given below:
There are also the following Hyperspectral metrics (derived from .bil files & .igm files)
- Hyperspectral Mean
- NDVI
- A single hyperspectral band
.png)
Polygonal Meshes
.tif)
The following video was also rendered in Maya using a polygon exported from DASOS:
List of Feature Vectors
This is useful for characterising object inside the 3D space (e.g. trees). For each column of the voxelised FW LiDAR, information around its local area are exported. The exported format is .csv to easy usage in common statistical packages like R and matlab.
In the following images there are two output examples. The top on contains processed information about the data and the second file contains the raw intensity values. Additionally each line is a feature vector.
Related Links
https://github.com/Art-n-MathS/DASOS/blob/master/DASOS_userGuide_v2.pdf
The windows executable and the code are available at:
https://github.com/Art-n-MathS/DASOS
To add your own customised 2D metrics, you may read the following tutorial:
http://miltomiltiadou.blogspot.co.uk/2016/07/how-to-add-metrics-to-dasos.html
For any questions regarding the usage of the software please use the following Google Group:
https://groups.google.com/forum/#!forum/dasos---the-native-full-waveform-fw-lidar-software
Updates about DASOS could be found @_DASOS_ on twitter
For more information about the algorithms please refer to the related paper:
https://www.researchgate.net/publication/334069759_Open_source_software_DASOS_efficient_accumulation_analysis_and_visualisation_of_full-waveform_lidar
Related Papers:
Acknowledgements
I would also like to thanks my initial and current supervisors Dr. Matthew Brown and Dr. Neill D.F Cambpell and all the people who occasionally got involved: Dr. Mark Warren, Susana Gonzalez Aracil, Dr. Daniel Clewley and Dr. Darren Cosker.
This project is funded by the Centre for Digital Entertainement and Plymouth Marine Laboratory.
The data used were collected by the NERC Airborne Research and Survey Facility (ARSF). Copyright is held by the UK Natural Environment Research Council (NERC).
Please note that this text was taken and modified from the EngD thesis of Milto Miltiadou, which was submitted to University of Bath in 2017.
