Remote Sensing: Segmentation and Classification of LIDAR Data


Marc Bartels and Hong Wei

Our research project is about segmentation and classification of remotely sensed LIght Detection And Ranging (LIDAR) data. Airborne laser scanned LIDAR is a relatively young medium in the field of photogrammetry. The accuracy in elevation of remotely sensed LIDAR data is a beneficial feature for various applications such as forestry, archaeology, 3D city map generation, flood simulations, coastal erosion monitoring, landslide prediction, corridor mapping and wave propagation models for mobile telecommunication networks and many more.

Contributing to these applications, our work aims to develop novel algorithms for terrain feature classification based on LIDAR data. Terrain features such as buildings, streets, railways and bridges, but also rivers, basins and vegetation are subject to our investigations. By using pattern recognition techniques, methods of photogrammetry and the unique height information of LIDAR data, segmentation and classification of terrain features is carried out in the course of the project. Our current investigations aim at terrain feature classification in LIDAR data by fusing several simultaneously recorded bands such as

We are looking for partners in industry, the public and academic sector who would like to collaborate with us. If you can provide LIDAR data samples with these or different characteristics as described above or are interested in writing joint publication, please do not hesitate to contact us.

Marc Bartels, LIght Detection And Ranging (LIDAR)

LIDAR Data Acquisition

The measurement technique using LIght Detecting And Ranging (LIDAR) was developed by the National Aeronautics and Space Administration (NASA) in the 1970s and has been commercially used since the early 1990s. A LIDAR data acquisition system consists of three elements: a Laser Range Finder (LRF), a Global Positioning System (GPS) receiver and an Inertial Navigation System (INS). While mounted on a platform like a plane as depicted in Figure 1, the distance between the instrument and a point on the surface is estimated (Z component) by measuring the time the laser pulse needs to hit the receiver of the laser instrument. GPS and INS complement the data sets with position components (X and Y) and orientation, respectively. Since only one measurement per X and Y component pair is estimated, LIDAR data is often referred as 2½ dimensional. Characteristic for LIDAR data is also that their elements are irregularly distributed in the first place and thus form a point cloud. Overlapping strips are usually acquired, when scanning the target landscape. These strips are then combined in post-processing steps, also known as strip adjustment.

DTM and nDSM generation from a DSM

Figure 1: Airborne LIDAR data acquisition

Generally, there are at least two types of echoes which can be recorded by a LIDAR system: the first and the last echo as schematically depicted in Figure 1. These two categories of responses are the basis for retrieving Digital Surface Models (DSM) and Digital Terrain Models (DTM). Acquired points of the category of first echoes mostly hit canopies, roofs or chimneys, whereas last echoes of the laser are reflections of the ground. Simultaneously, intensity data, that is the reflected signal power by the objects in the scanned area, can be recorded. This type of data is especially useful if different objects have the same height but different emission characteristics (e.g. water and road). Together with LIDAR intensity (reflectance) and multi spectral data recorded at the same time, segmentation and classification techniques can be exploited extensively. This complementary process of LIDAR data is also referred as Data Fusion.

Pseudo coloured height information of one LIDAR data tile

Figure 2: Pseudo coloured height information of one tile

Pseudo coloured gridded IDAR data tile

Figure 3: Pseudo coloured gridded IDAR data tile

Figure 2 shows the pseudo coloured height information of LIDAR data supplied by Environment Agency, UK. Noteworthy is the noise that generally comes with LIDAR data which makes the development of segmentation and classification algorithm challenging as well as the artefact "no data" as depicted in the lower right corner in Figure 3. Table 1 summarises both the advantageous but also less beneficial characteristics that make LIDAR special:

Table 1: Pros and cons of LIDAR data from a image processing point of view
Pro Con
no shadows no surface texture, no colour information
no horizontal occlusion vertical occlusion
high accuracy for less hilly terrain (10 cm - 15 cm) poor accuracy in hilly terrain (up to 200 cm)

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Marc Bartels, LIght Detection And Ranging (LIDAR)

Segmentation and Classification of LIDAR Data

The most common problem in LIDAR filtering is the separation of object and ground points resulting in DTM and nDSM generation. On principle, two approaches can be chosen to segment and classify LIDAR data: unsupervised classification (often referred as segmentation) and supervised classification (also classification). Regarding land cover classification, current investigations target employing Maximum Likelihood Classification. With respect to segmentation, our research involves textural analysis using Wavelets, Gabor Wavelets, modified histogram thresholding using Wavelet Packets and co-occurrence matrices. We also investigate on the separation of object and ground points in LIDAR data (irregular and gridded) using Skewness Balancing. For further information, please refer to our publications.

Figure 4a illustrates the generation of a Digital Terrain Model (DTM) from a Digital Surface Model (DSM) for an artificial height scene characterised by sloped terrain and several buildings in Figure 4a using Skewness Balancing. The normalised Digital Surface Model (nDSM) in Figure 4b is obtained by subtracting the DTM from the DSM.

DTM and nDSM generation from a DSM

Figure 4: Generation of a DTM and an nDSM from a DSM

Figure 5 depicts a LIDAR data tile of an urban area with detached objects (both buildings and vegetation of different height) and the scene after applying Skewness Balancing. The data was kindly provided by TopoSys GmbH, Germany, by courtesy of the Stadt Mannheim, Germany, the copyright holder ©. It can clearly be seen that all detached object have been removed. Further details can be found in our paper Segmentation of LIDAR Data using Measures of Distribution, presented at the ISPRS Mid-term Symposium 2006 "Remote Sensing: From Pixels to Processes", Enschede, the Netherlands, 8-11 May 2006.

DSM of LIDAR data tile

Figure 5: Object and ground point in LIDAR data separation using Skewness Balancing

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Marc Bartels, LIght Detection And Ranging (LIDAR)

LIDAR related Publications and Presentations


Marc Bartels, LIght Detection And Ranging (LIDAR)

Project related Awards

Poster Paper Prize - Merit Award - at the RSPSoc 2006 at Fitzwilliam College, University of Cambridge, for the paper

M. Bartels and H. Wei.
Rule-based Improvement of Maximum Likelihood Classified LIDAR Data Fused with Co-Registered Bands.
Annual Conference of the Remote Sensing and Photogrammetry Society, CD Proceedings, 2006
Cambridge, UK, 05-08 September 2006

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Marc Bartels, LIght Detection And Ranging (LIDAR)

Contact Details

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Marc Bartels
School of Systems Engineering
Computational Vision Group
The University of Reading
Whiteknights
Reading
Berkshire
RG6 6AY
United Kingdom

+44(0)118 378 7633

mail_marc_bartels

   
   
   
   
   
   
   
   
   

   

   
   
   
   
   
   
   
   
   

   

Hong Wei
School of Systems Engineering
Computational Vision Group
The University of Reading
Whiteknights
Reading
Berkshire
RG6 6AY
United Kingdom

+44(0)118 378 8608

mail_hong_weil

   
   
   
   
   
   
   
   
   

   

Hong and Marc presenting a poster at the ICPR 2006, Hong Kong, China.

Hong Wei and Marc Bartels at the ICPR 2007.


Marc Bartels, LIght Detection And Ranging (LIDAR)

Acknowledgements

We would like to take the opportunity to thank our supporters to this project. The project is RETF funded by the University of Reading and has been supported with conference and travel grants by the School of Systems Engineering, the University of Reading, the RSPSoc, UK and IAPR. We would like to thank the data providers for LIDAR data supply, in particular

Without their data, these studies would have been impossible. We would like to acknowledge especially the contributions of the above mentioned organisations and companies to flood prevention and forestry amongst other environmental services. They are creating a sustainable heritage of unpayable value to society.

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Marc Bartels, LIght Detection And Ranging (LIDAR)
Last update: 16/02/2009