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Comparing endmember extraction methods based on CASI-1500hyperspectral imagery for seagrass classificationDanial Mariampillai & Su-Yin TanDepartment of Geography and Environmental Management, University of Waterloo, Waterloo, N2L 3G1, Canadadumariam@uwaterloo.ca, su-yin.tan@uwaterloo.caAbstractClassification of submerged aquatic vegetation (SAV) in coastal zone mapping is an important aspect of itsmanagement, supporting decisions made in spatial marine planning, design and implementation. Remote sensing,which involves the measurement of electromagnetic radiation reflected or emitted from the Earth’s surface isutilized by digital satellite or airborne instrumentation. These technologies assume critical roles in providing thisinformation through the exploitation of spectral energy detected by passive sensors. Airborne hyperspectral imagery(HSI) can be used for creating SAV maps within a relatively small time frame, utilizing endmember extractionalgorithms and automated supervised classification methods. The goal of this research is to compare differentmethods of endmember selection for detecting seagrass for the purposes of mapping its spatial coverage and speciesidentification.IntroductionSubmerged aquatic vegetation (SAV), such as seagrass is vital to marine habitats in shallow water systems(Bostater et al., 2004). Areas such as estuaries and coastal zones are complex and vast environments providingchallenges to those who study and manage these locations. Geospatial mapping technologies are necessary forcontinuous monitoring of these systems, thus providing valuable data for policy-making and scientific purposes.Remote sensing techniques, such as aerial and digital satellite imagery have critical roles in providing mapinformation related to geomorphologic zones and biodiversity within coastal areas (Coleman et al., 2011). Anincreasingly popular remote sensing configuration today utilizes multiple sensors on airborne platforms that cancollect a variety of data types within a relatively small time frame (Coleman et al., 2011).This study examines the use of the Compact Airborne Spectrographic Imager (CASI)-1500 for collectinghyperspectral imagery (HSI) for SAV classification through the application of endmember extraction techniques, awidely accepted approach for classifying mixed pixel images (Plaza, et al., 2004). The CASI-1500 sensor utilizedfor this project is part of the Compact Hydrographic Airborne Rapid Total Survey (CHARTS) system that is jointlyoperated and maintained by the U.S. Army Corps of Engineers (USACE) and U.S Naval Oceanographic Office(NAVO). The imagery, supplemental data, and ground truth information was provided by the Joint Airborne LiDARBathymetry Technical Center of Expertise (JALBTCX), based in Kiln, Mississippi, USA.The intended methodology utilizes endmember extraction algorithms (i.e. the process of distinguishing thedifferent spectra that can then be grouped into classes and mapped) on data collected over Plymouth Harbour andButtermilk Bay located in Massachusetts, USA. Utilizing the set of tools available through the image processingsuite ENVI 5.0, the pixel purity index (PPI) and sequential maximum angle convex cone (SMACC) were used toidentify and map the seagrass cover, as well as to determine whether the methods could reliably distinguish betweendifferent SAV species.The two study areas set the extent of a developed analytical framework for the measurement and analysis ofspectra in the acquired images. A supervised classification routine called spectral angle mapper (SAM) is thenemployed to determine the robustness of the defined imaging spectroscopy methods. Verification was achievedthrough an accuracy assessment, where the ground truth data is compared against the classifications produced by theSAM process.210
Background11 th International Symposium for GIS and Computer Cartography for Coastal Zones ManagementStudies are projecting that within the next 10 to 15 years, populations residing within 100 km of the coastline willbe estimated to be 2.75 billion (Collin et al., 2007). As a large proportion of the human population and vast naturalresources occur in coastal regions, it embraces environmental and political significance and therefore needs to bemanaged (Woodroffe, 2003).A considerable amount of literature has been published which demonstrates that SAV, such as seagrass, providesa variety of economical and ecological benefits (Anstee et al., 2001; Bostater et al., 2004; Dekker et al., 2010; Casalet al., 2011). However, marine and coastal systems historically lack the quantification of patterns and processes thatexist on the terrestrial side of mapping due to insufficient field and remote observational data (Collin et al., 2007).Using known statistical patterns, commercial seafloor and habitat classifiers have been developed (e.g., RoxAnnSystems) and implemented through traditional shipboard methods (Collin et al., 2007). These methods have to dealwith limitations of the sea where shipboard methods cannot access or survey the required area effectively due tovarying environmental conditions and natural hazards. Using an airborne survey platform can overcome theseobstacles.HSI is a remote sensing technology that can be used to accrue images in many ranges of the spectrum, being ableto collect 200 or more bands of data and producing contiguous, high resolution radiance spectra, instead of discretemeasurements that average radiance over a wide range of spectral bands. The system is mounted on an airborneplatform, using a push broom scanner radiometer, where each channel in the push broom scanner records an imagewithin its configured narrow spectral channel (Rzhalnov et al., 2012). The CASI system for the project wasconfigured to collect 36 spectral bands at a 1.8 nm bandwidth between the wavelengths of 380 to 1050 nm.MethodologyHSI data supplied by JALBTCX can be exploited for various coastal applications, such as seagrass mapping.However, before the data is useful, all hydrographic mapping techniques that use airborne remote sensors todelineate the benthic environment are pre-processed for radiometric, atmospheric and geometric correction. Forexample, adjusting for the attenuation effects from the water column is a significant challenge when mapping thistype of environment (Zulhaidi et al., 2007).Linear spectral unmixing has become a standard approach for the classification and analysis of HSI data(Martinez et al., 2006). As pixels in an HSI are not discrete, an unmixing of the image can occur to separate thedifferent fractions of more than one substance within a pixel (Keshava, et al. 2002; Martinez et al., 2006). Onceseparated, the distinct spectra (endmembers) can be grouped into classes and mapped (Keshava, et al. 2002).In this study, PPI and SMACC were used to examine and to determine the endmembers within the HSI over thestudy area. This can be achieved by initially eliminating any data redundancy and noise in the images so that thealgorithms can effectively determine what substances are present and estimate the abundance of each mixed pixel inrelation to the spectra and determined endmembers (Keshava et al., 2002).Once the spectral classes were grouped by endmember processing, the image was classified using the SAMmethod. The SAM is designed for use with HSI and is an automated classification process that has been specificallydeveloped to map the spectral similarities in an image with field data (Alberotanza et al., 1999; Jollineau et al.,2008). This can be achieved by comparing the unknown information in the pixel of the image to the determinedendmembers (Jollineau et al., 2008). The similarity is observed by the spectral angle between them (Alberotanza etal., 1999). The advantage of using this methodology for classification is that not all the classes need to be knownbeforehand, which is not always possible (Zulhaidi et al., 2007; Jollineau et al., 2008). A thorough accuracyassessment was also developed to test the validity of results and to compare to the ground truth provided byJALBTCX for the project.Results (work in progress)Previous research has demonstrated the ability to utilize sensors, such as the CASI-1500, which collects HSI andis available on the CHARTS system to map seagrass. HSI can be used in conjunction or as a standalone product tomap and classify the seafloor accurately (Brando et al., 2003). The methods discussed aim to show that there is avaluable role for HSI in the various needs of coastal zone management, especially in SAV mapping. This will be211
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CoastGIS Conference 2013: Monitorin
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Sponsors
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Steering Committee Secretariat main
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Santos Basin, BrazilA.F. Romero, R.
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Coastline development and associate
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Use of Terrestrial-LiDAR for quanti
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Community beach monitoring: utilisi
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ResultsMetadata11 th International
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Coastal vulnerability index to glob
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generating probabilities contours f
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maps, the most critical conditions
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Statistical and spatial toolbox for
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Global oceans and marine planning
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Analysis11 th International Symposi
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Building scenarios and visualizatio
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Using a 3D physics-based visualizat
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Documenting situated tsunami risk p
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SeaSketch11 th International Sympos
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A structured model to enable coasta
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A dynamic GIS as an efficient tool
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GIS spatio-temporal modeling of hum
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ResultsCollected data11 th Internat
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The use of GIS and geospatial techn
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Ocean Radar for Monitoring of the C
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Geophysical investigations of marin
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A semi-supervised learning framewor
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Simulation of maritime paths taking
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COINAtlantic: Sharing through open
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Perspectives11 th International Sym
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