Seabed Classification Using LIDAR Data
New Process Generates Seabed Classes from Tenix LADS LIDAR Data
Following the successes of single-beam and swath classification technology, Quester Tangent in partnership with Tenix LADS and the Geological Survey of Ireland has introduced LIDAR Seabed Classification. This new capability will significantly enhance the LIDAR data product in support of large scale shallow water mapping for bathymetric and habitat studies.
Bill Collins – Quester Tangent; Mark Penley– Tenix LADS Corporation ; Xavier Monteys – Geological Survey of Ireland
It is well known that the statistical characteristics of a sonar backscatter image depend on the bottom type. Even to a novice user, the texture differences between images of rocks, sand, and mud are readily apparent. Statistical processing captures many of the pertinent details of the interaction between the sound and the bottom and of its vertical relief. Multivariate statistics can then isolate those details that are rich in information about the bottom, producing features that contain the information necessary for accurate and reliable bottom classifications. With the advance of airborne laser bathymetry technology it is now possible to capture the reflectivity along with the depth from each seabed footprint. In a method very similar to sonar image classification, the reflectivity data can be transformed into bottom type. In 2006 Tenix LADS collected LIDAR data from three embayments on Ireland’s west coast. This article presents class maps from those surveys.
The Laser Bathymetry Process
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| Figure 1. Time series of optical energy returned to the receiver. Note the spike representing the sea surface and the seabed surface. Shown in green is the energy on which the estimate of reflectivity for classification is based. Click image to enlarge. |
Airborne Laser Bathymetry (ALB) or Airborne LIDAR (Light Detection and Ranging) is a remote sensing technique that, after years of research and public sector operation, became commercially available in the late 1990's. These systems are well suited for surveying shallow, clear coastal waters and in the right environmental conditions have significant efficiency and safety advantages over survey by surface vessels. The original use of these systems was for the collection of bathymetric data for nautical charting applications. This application remains the dominant commercial requirement for ALB surveys, however as with all remote sensing technologies, there has been a trend to enhance bathymetry data with complimentary data sets such as reflectivity. The reflectivity of an ALB pulse is a measure of the amount of energy reflected from the seabed for each individual laser pulse at the wavelength of the laser, 532 nm (green/blue). The basic difference between processing an ALB waveform for depth and reflectivity is that depth processing focuses on the leading edge of the return waveform and reflectivity requires integration of the entire return pulse from the seabed (see Figure 1). 
Quality Control, Compensation, and Normalisation
Each sounding is assessed for suitability. Drying soundings and sounding in very shallow water are not processed for reflectivity. The entire waveform from only the most suitable LIDAR returns needs to be compensated for the electronic gain of the receiver system. The gain control algorithms of the LADS system are complex and not detailed here. This step is straight forward with each sounding normalised for the electronic gain that was applied to the photo multiplier tube to which the received laser energy is optically routed. ;
The gain-normalised return waveform is then analysed to determine the energy returning from the seabed. As indicated previously this is just a simple integration of the segment of the waveform reflected from the seabed. The integration of the waveform from the seabed will produce a numerical value of reflectivity. To ensure this value accurately and meaningfully described variation of seabed reflectivity several parameters must be taken into consideration. Energy is lost from the pulses transmitted from the aircraft. These losses include the air/water interface and losses through the water column as well any system specific losses such as optical filtering and receiver field of view.
A reflectivity value, calculated for each pulse, is the ratio between the received energy normalised for the losses described and the transmitted energy. In the LADS algorithms the reflectance is treated as a relative value. This is considered a more robust solution given the complexity in modelling the losses through the water column and at the water / air interface. Indeed this solution comprises a similar"phenomenological" approach to that of many swath and single beam classification systems
Once a relative reflectivity value is calculated further statistical cleaning to remove outliers is completed. Because the data set is of relative reflectivity rather than an absolute value for each point the entire data set is scaled to ensure the full dynamic range is used over the data set This scaling is applied over an entire survey area to ensure consistency of the data set
The Classification Process
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| Figure 2. Cartoon of a LIDAR survey by TenixLADS Dash-8 aircraft showing seabed reflectivity. The image is divided into patches to which a seabed class is assigned. |
Reflectivity values for each line are assembled into images, one pixel per footprint (see Figure 2). Each image is divided into small squares, nine pixels on a side. The squares are placed only where the reflectivity values are present and meet quality standards (refer to Figure 3). These reflectivity images are analysed using features that capture amplitude (or reflectivity), its distribution and its texture. Texture is an important property of images and can be visualised as the roughness and pattern differences between real surfaces. Texture is a second-order statistic of a matrix, meaning that it is based on relationships between a pixel and its neighbours, that is, order is important. In addition to image features depth information is used to generate features based on sea floor relief. In total 137 features are used to describe each image patch.
Classification of the sea bottom that gave rise to these features is done by an automated clustering method that adapts to the characteristics of the data set. Each cluster represents a bottom type, which can be labelled based on ground truth; for example, photographs, grain-size analysis, or other local data. If the bottom type is known before classification, data from the areas of known sediment type can be used to build a catalogue, which would then be used to classify subsequent or archived data.; This is called supervised classification. The alternative, unsupervised classification forms the data into logical clusters that can then be identified based on ground truth. The effectiveness of unsupervised classification in uncovering practical and valuable information from imagery has been demonstrated in many projects.
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| Figure 3. Reflectivity mosaic with squares representing 9 by 9 pixel arrays from which features will be extracted. |
Typical deliverables for seabed classification processing include a point data set with each record containing georeferenced attributes including seabed class with associated confidence and probability values. The point data can be taken into GIS or surface modelling software to present the data as classification maps overlaying bathymetric terrain models.
Mapping the Irish Offshore Shallow Environments
In the summer of 2006 Tenix LADS Corporation was contracted to fly LIDAR bathymetry over three bays on the west coast of Ireland. The mission was in support of the Irish INFOMAR program (the successor to the Irish National Seabed Survey). The INFOMAR goals are to map the highest priority areas of coastal Ireland using a variety of mapping techniques including LIDAR, sonar and direct investigative methods. INFOMAR directed Tenix LADS to generate seabed classification data as part of the LIDAR project deliverables.
The results were variable due to unseasonably poor weather conditions and thus water conditions. Sorties were flown over Dunmanus, Bantry and Galway Bays. The laser gathered data down to about 20metres water depth. Bathymetry and classification results from Galway Bay are shown (see Figures 4 and 5).
Approximately 150,000 seabed class data points were generated from the three embayments. The data were categorically interpolated using QTC CLAMS to reduce variability and produce images. The images were draped on a digital terrain model for presentation and quality control.
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| Figure 4. LIDAR bathymetry of Galway Bay (source INFOMAR 2006). Click on image to enlarge. |
Figure 5: SeabedClassification of Galway Bay using LIDAR reflectivity. There are ten classes with optically similar classes being assigned similar colours. Click on image to enlarge. |
Future Research
Quester Tangent and the Irish Geological Survey in partnership with the Irish Marine Institute have an ongoing program of research into LIDAR Classification within the INFOMAR Programme. The research goal is to improve the classification process by identifying algorithms and features that are better suited to LIDAR reflectivity imagery, including bathymetry related algorithms. The research will also investigate seabed properties to which the LIDAR reflectivity responds including sediments and their biological constituents. One of the most challenging research areas for2007 will be the validation of the classification by means of direct sampling,video and aerial photo. The results will also be compared to acoustic seabed classification for these areas.
Acknowledgements
The authors wish to acknowledge the support of the INFOMAR Programme and its supporting agencies including the Marine Institute and the Geological Survey of Ireland. The industrial partners are Tenix LADS Corporation of Australia and Quester Tangent of Canada.
Biographies
Bill Collins
Bill is a Marine Geologist and a managing director of Quester Tangent. He has been with the company for more than 10 years following work with the Ocean Drilling Program at the University of Washington in Seattle and SOPAC in Fiji. Bill has published extensively in the field of seabed mapping. More ...
Mark Penley
Mark is the Technical Manager for Tenix LADS Corporation. He has over 20 years of industry experience and has been with Tenix LADS Corporation for the last 12 years. Mark has a Bachelor of Electronic Engineering with specific experience in electro-optic design, analogue and digital control systems and system engineering integration.
Xavier Monteys
Xavier is a Marine Geologist in the Geological Survey of Ireland. Over the last six years he has been involved in mapping the Irish offshore waters using different platforms.
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