Research papers Unsupervised fuzzy classification and object-based image analysis of multibeam data to map deep water substrates, Cook Strait, New Zealand Vanessa Lucieer a,n , Geoffroy Lamarche b a Tasmanian Aquaculture and Fisheries Institute (TAFI), University of Tasmania, Private Bag 49, Hobart, Tasmania 7001, Australia b National Institute of Water and Atmospheric Research (NIWA), Private Bag 14-901, Wellington 6041, New Zealand article info Article history: Received 10 November 2010 Received in revised form 28 April 2011 Accepted 29 April 2011 Available online 12 May 2011 Keywords: Segmentation Backscatter Bathymetry Habitat mapping Fuzzy-c-means abstract A comprehensive 32 kHz multibeam bathymetry and backscatter survey of Cook Strait, New Zealand ( 8500 km 2 ), is used to generate a regional substrate classification map over a wide range of water depths, seafloor substrates and geological landforms using an automated mapping method based on the textural image analysis of backscatter data. Full processing of the backscatter is required in order to obtain an image with a strongly attenuated specular reflection. Image segmentation of the merged backscatter and bathymetry layers is constrained using shape, compactness, and texture measures. The number of classes and their spatial distribution are statistically identified by employing an unsupervised fuzzy-c-means (FCM) clustering algorithm to sediment samples, independent of the backscatter data. Classification is achieved from the overlay of the FCM result onto a segmented image and attributing segments with the FCM class. Four classes are identified and uncertainty in class attribution is quantified by a confusion index layer. Validation of the classification map is done by comparing the results with the sediment and structural maps. Backscatter (BS) strength angular profiles are used to show acoustic class separation. The method takes us one step further in combining multibeam data with physical seabed data in a complementary analysis to seek correlations between datasets using object-based image analysis and unsupervised classification. Texture within these identified classes is then examined for correlation with typical backscatter angular responses for mud, sand and gravel. The results show a first order correlation between each of the classes and both the sedimentary properties and the geomorphological map. & 2011 Elsevier Ltd. All rights reserved. 1. Introduction Quantifiable methods that map benthic substrates from remotely sensed data have become a significant focus of the seafloor map- ping community (e.g., Brown and Blondel, 2009; Le Bas and Huvenne, 2009; Lucieer and Lucieer, 2009; Wright and Heyman, 2008). Seafloor sediments and geomorphology are valuable proxies for: (i) substrate composition and sedimentary processes; (ii) tectonic processes; (iii) benthic habitat (Brown and Blondel, 2009; Kostylev et al., 2001; Orpin and Kostylev, 2006; Ryan et al., 2007) and (iv) benthic biodiversity (Schlacher et al., 2009; Thrush et al., 2006). Moreover, knowledge of the distribution of these proxies informs species management and the development of marine protected areas (Anderson et al., 2008, 2007; Jordan et al., 2005; Kostylev et al., 2003; Lucieer and Pederson, 2008). Acoustic backscatter images generated from multibeam echo sounder (MBES) data are often complex due to variability in seafloor roughness and impedance, sediment grain size and volume heterogeneity, as well as the inherently noisy signal at nadir and steep grazing angles (Marsh and Brown, 2009). For these reasons, pixel-based classification schemes are critically limited when differentiating seabed zones with well-defined boundaries, resulting in inconsistent classifications that are highly biased by the specular response at nadir. In contrast, object-based image analysis techniques overcome these difficulties by first segmenting the image into meaningful seabed zones of various sizes, based on their spectral and spatial characteristics (Blaschke, 2010). Such classification methods, originally developed for remotely sensed data from satellites, have been successfully applied to multibeam and sidescan sonar data (Lucieer, 2007, 2008; Preston, 2009). In this paper we apply an object-based image analysis technique to segment and classify multibeam backscatter data from Cook Strait, New Zealand, to generate a first-level quantitative seabed substrate map. The diverse geological, hydrodynamic and ecological environ- ments of Cook Strait, together with comprehensive multibeam bathymetric (Fig. 1) and backscatter coverages (Fig. 2), alongside a Contents lists available at ScienceDirect journal homepage: www.elsevier.com/locate/csr Continental Shelf Research 0278-4343/$ - see front matter & 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.csr.2011.04.016 n Corresponding author. Tel.: þ61 3 62277219. E-mail addresses: vanessa.lucieer@utas.edu.au (V. Lucieer), g.lamarche@niwa.co.nz (G. Lamarche). Continental Shelf Research 31 (2011) 1236–1247