Monday, 23 March 2015

DASOS - Open Source Software for processing full-waveform LiDAR and hyperspectral Images

What is DASOS?

DASOS is an open source software that aims to ease the way of handling the full-waveform LiDAR data. Its name was derived from from the Greek word δάσος (=forest). It was first presented at the International Symposium of Remote Sensing of the Environement, 2015 (Alignment of Hyperspectral Imagery and full-waveform LiDAR data for visualisation and classification purposes). Its last feature was presented at the journal paper Detection of dead standing Eucalyptus camaldulensis without tree delineation for managing biodiversity in native Australian forest.

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

Here is a list of the available 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
- A single hyperspectral band

Polygonal Meshes

The following example is from New Forest and generated at one meter resolution.The first one was generated using FW LiDAR data and three user defined bands of the hyperspectral images. The second one was generated with only the FW LiDAR data.

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

The full user guide, the windows executable and the code are available at:

To add your own customised 2D metrics, you may read the following tutorial:

For any questions regarding the usage of the software please use the following Google Group:!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:
Miltiadou M., Warren M. A., Grant M., Brown M., 2015, Alignment of Hyperspectral Imagery and full-waveform LiDAR data for visualisation and classification purposes, 36th International Syposium of Remote Sensing of the Environment.


Special thanks are given to my industrial supervisor Dr. Michael Grant, who has supported me during my entire studies.

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.

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