Bathymetric data depicts the geomorphology of the seabottom and allows characterization of spatial distributions of apparent benthic habitats. The variability of seafloor topography can be defined as a texture. This prompts for the application of well developed image processing techniques for automatic delineation of regions with clucially different physiographic characteristics. In the present paper histograms of biologically motivated invariant image attributes are used for characterization of local geomorphological feahires. This technique can be naturally applied in a range of spatial scales. Local feature vectors are then submitted to a procedure which divides the set into a number of clusters each representing a distinct type of the seafloor. Prior knowledge about benthic habitat locations allows the use of supervised classification, by training a Suppolt Vector Machine on a chosen data set, and then applying the developed model to a full set. The classification method is shown to perform well on the multibeam echosounder (MBES) data from Piscataqua River, New Hampshire, USA

Cutter, Randy G, Jr.

Mayer, Larry A.

Rzhanov, Yuri

English

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University of New HampshireUniversity of New Hampshire Scholars' RepositoryCenter for Coastal and Ocean Mapping Center for Coastal and Ocean Mapping2003Seafloor Segmentation Based on BathymetricMeasurements from Multibeam EchosoundersDataYuri RzhanovUniversity of New Hampshire, Durham, Yuri.Rzhanov@unh.eduRandy G. Cutter Jr.University of New Hampshire, DurhamLarry A. MayerUniversity of New Hampshire, larry.mayer@unh.eduFollow this and additional works at: https://scholars.unh.edu/ccomPart of the Computer Sciences Commons, and the Oceanography and Atmospheric Sciences andMeteorology CommonsThis Conference Proceeding is brought to you for free and open access by the Center for Coastal and Ocean Mapping at University of New HampshireScholars' Repository. It has been accepted for inclusion in Center for Coastal and Ocean Mapping by an authorized administrator of University of NewHampshire Scholars' Repository. For more information, please contact nicole.hentz@unh.edu.Recommended CitationRzhanov, Yuri; Cutter, Randy G. Jr.; and Mayer, Larry A., "Seafloor Segmentation Based on Bathymetric Measurements fromMultibeam Echosounders Data" (2003). International Symposium on Signal Processing and Its Applications (SPIA). 283.https://scholars.unh.edu/ccom/283SEAFLOOR SEGMENTATION BASED ON BATHYMETRIC MEASUREMENTS FROM MULTIBEAM ECHOSOUNDER DATA K Rzhanov, G. R. Cutter: L. A.  Mayer Center for Coastal and Ocean Mapping (C-COM) University of New Hampshire Durham 03824, USA email: yuri.rzhanov@unh.edu, gcutter@cisunix.unh.edu, Imayer@cisunix.unh.edu ABSTRACT Bathymetric data depicts the geomorphology of the seabot- tom and allows characterization of spatial distributions of apparent benthic habitats. The variability of seafloor to- pography can be defined as a texture. This prompts for the application of well developed image processing techniques for automatic delineation of regions with clucially differ- ent physiographic characteristics. In the present paper his- tograms of biologically motivated invariant image attributes are used for characterization of local geomorphological fea- hires. This technique can be naturally applied in a range of spatial scales. Local feature vectors are then submitted to a procedure which divides the set into a number of clus- ters each representing a distinct type of the seafloor. Prior knowledge about benthic habitat locations allows the use of supervised classification, by training a Suppolt Vector Ma- chine on a chosen data set, and then applying the developed model to a full set. The classification method is shown to perform well on the multibeam echosounder (MBES) data from Piscataqua River, New Hampshire, USA. 1. INTRODUCTION Seafloor data (bathymetty of backscatter maps) can be rep- resented as twodimensional raster datasets. prompting for direct application of image analysis methods. For exam- ple, pattern-matching techniques can be applied to search for particular features of interest. Automation of these tasks leads to reduction of processing time and analyst errors, compared to manual processing, provides more reproducible results, reduces subjectivity, and eventually allows unsuper- vised exploration by’unmanned underwater vehicles. Seafloor bathymetric patterns relate to geological, hy- drodynamic and biological processes, and are dependent upon and distinct to particular spatial scales. At a partic- ular scale, seafloor textures (configuration patterns) can be interpreted with respect to geological processes or benthic habitats [ I .  21. Texture analysis has been useful for au- tomated segmentation of seafloor bathymetry for benthic habitat mapping and for delineating regions of apparently similar substrate composition. However, the interpretation of texture features in terms of basic seafloor configuration properties commonly of interest (e.g. roughness, orienta- tiodisotropy) can be difficult. Texture features do not often provide intuitive measures of seafloor Characteristics and therefore may appear to incorporate subjectivities and lack acceptance. Texture features may fail to be convincing, re- gardless of resultant segmentations, if they cannot be ex- pressed as inherent measures of particular physical seafloor characteristics. In this paper, the attempt was made to derive quantita- tive measures from bathymetric dataset that would convey such properties as local gradients and curvatures, that are intuitively understandable to geologists, geophysicists and biologists. Image invariant attributes considered below are such measures. Development of methods for matching images acquired by non-calibrated cameras have lead to design of algorithms for calculation of local characteristics of two dimensional raster data sets, invariant to rotation, scaling, and weak affine distortions (see, for example, [3,4]). Typically, these meth- ods involve a finite set of points (interest points) at which certain local functional is maximized, and for each point a vector of features (invariants) is computed, allowing corre- spondences to be found between sets of points for two (or more) images. The neighborhood of any point can be de- scribed by a set of local derivatives, so called ”local jet” [3]. The reliable calculation of this set is achieved by convolu- tion of data points with Gaussian derivatives, which intro- duces a characteristic scale (smoothing scale, or size of the Gaussian) (Figure 1). In our implementation there is no need to detect special points - all data points are of equal significance. The goal is to compute local data characteristics that are not dependent on the chosen (X, Y) system of coordinates. This can be achieved by conversion to Gauge coordinate system ( q , < ) ,  where one axis is along the direction of maximum gradi- 0-7803-7946-2/03/517.00 02003 IEEE 529 Figure 1: Kernels for calculation of Gaussian derivatives: L, Lz, L,, Lzy, Lm, &J. ent (unit vector q = VIJIVII), and another is orthogonal to the first (Flq) .  The local jet of order 2 is represented by 5 values L,L,, L,,, Le,, LEE (by definition, Le = 0). For bathymetric data convolution with a Gaussian, L, repre- sents simply a smoothed depth and cannot be considered an invariant. Four other local invariants well known in differ- ential geometry have been used (I,,, + ICE)  (Laplacian), I, (gradient magnitude), IEEII ,  (isophote curvature), It,/I, (flowline curvature). These values can be expressed in terms of Cartesian derivatives: Use of invariant image attributes (IIA) for characteriza- tion of seafloor geomorphology has significant advantages in comparison with a texture feature classification scheme known as the local Fourier histogram (LFH) method [5,2]. While the magnitudes of Fourier coefficients reflect only roughness of the terrain on a variety of scales, invariant at- trihutes reflect a number of local properties of the spatial data that could be essential for habitat characterization. For example, gradient magnitude reflects local steepness, and isophote curvature is a natural ridge detector. At the same time these attributes have a p r o p e q  of ro- tation invariance - similar to LFH approach. This property is of utmost importance, as rasterisation of the spatial data is done without consideration of data itself, but along ar- bitrarily chosen axes. Note that the technique described in this paper can he applied to completely unstructured set of soundings, unlike the LFH method for which a regular grid is required. In characterization of a seafloor area, however, it is not a set of invariants for a single point that contain informa- tion about the geomorphology, but statistics of invariants for the area. So, following the approach proposed in [5], a set of the above invariants for every grid point within a square block of size N (thus introducing another measure- ment scale) are calculated and a histogram consisting of 8 bins for each invariant is constructed (feature vector con- sisting of 32 elements was found to be sufficiently long to reflect regions’ variability). To construct comparable his- tograms, it requires that the hounding values, within which each invariant is varying, are fixed. 2.5 and 97.5 percentiles of the entire distribution were used as these values in order to eliminate the influence of possible outliers. The 8-bin histograms for each of four invariants are normalized and concatenated as in [5], to produce attribute vectors of length 32, for each block of the data. Vectors are submitted to a clustering procedure, which divides them into requested number of groups. Agglomerative hierarchical clustering (AHC) [7] as well as fuzzy K-means clustering [8] were tested with similar results. Reported here are only the AHC results. The data used in this study was collected in the mouth of the Piscataqua River, flowing between New Hampshire and Maine, USA, with a Reson 8125 multibeam echosounder as part of the Shallow Water Survey 2001 Common Dataset. The bathymetry is presented on a UTM projection, zone 19 north. The data is gridded with 1 meter resolution and cov- ers approximately 800 m hy 1000 m. The greyscale-coded Digital Elevation Map of the area is shown in Fig. 2. Figure 2: Digital elevation map of the studied area. Shal- lower depths correspond to darker shades of grey. Black color corresponds to the absence of data. Feature vectors for classification by clustering were cal- culated for wety 10 x 10 block of the data (if more than 70 percent of observations were valid) - comprising 6880 vectors. AHC produced the class map shown in Fig. 3, left with colors corresponding to different classes of the seafloor 530 shown in Fig. 2. 2. JNTERPRETATION AND DELINEATION Surficial substrates in the study area were mapped by Ward [9] without the benefit of multibeam sonar data. Three primary substrate were determined from grab samples and sidescan sonar records: sand belt, bedrock regions and gravel else- where. Examination of the MBES data suggests that the map from [9] could be refined, and that additional sediment types probably exist. The intention of feature vector seg- mentation was to distinguish these substrate classes. Figure 3: Lefi: Class map - result of agglomerative hierar- chical clustering. Right: Feature vectors corresponding to cluster’s centroids, color-coded accordingly to colors used in left image. Shown in the right part of Fig.3 are average class vectors for the six cluster groups. (Six classes were chosen based on the branching pattern of the dendrogram from the clus- tering process.) These vectors can be used for classification of data from a different set. It is however necessary that conditions of calculation of histograms must be exactly the same: bathymetry is gridded with the same spatial resolu- tion, kernets for Gaussian derivatives have the same size, and same ranges for histogram construction must be used. To classify a new vector, Euclidean distances between it and each class vector (shown in 3) are calculated -the small- est distance decides the class. Note that as all elements in a vector represent bins in normalized histograms, Euclidean distance is an appropriate measure in this feature space. 3. SUPPORT VECTOR MACHINE CLASSIFICATION Often certain prior knowledge exists about the spatial loca- tions of different benthic habitats and this allows the appli- cation of supervised classification methods. In this paper we report the results obtained from training Support Vector Machine (SVM) on a chosen subset of data and use of a de- veloped model for classification of the whole set. Recently SVM classifiers have attracted much attention due to rela- tive simplicity in application and good results achieved in a variety of problems [IO,  I I]. SVMs are designed to solve two-class problems. Given a set of feature vectors repre- senting a class, and the set which is not, SVM training finds a ”decision surface” - hyperplane in the feature space - sep- arating hyperpoints of the first set from the second. Usually both sets of n-length vectors 6 are presented as a single set of (n + 1)-length vectors, with the last element (also often denoted as yi) being either +1 (meaning that vector belongs to the class), or -1. Unlike the clustering techniques, SVMs do not assume a model for the probability distribution ofthe data, only that it is fixed. SMVs are designed to minimize the structural risk, i.e., the probability of misclassification of a test feature vec- tor, given the training set. The term ”hyperplane” implies that SVM is a linear technique, however this deficiency can be easily overcome. Data sets that are not linearly separable in the original feature space of dimension n can he nonlinearly mapped onto another, N-dimensional space, where training by a lin- ear learning machine becomes possible: This mapping does not not increase the complexity of the problem, due to specifics of kernel machines, like SVM. Kernel machines can be formulated in two representations: ”primal” and ”dual”. In the dual representation the clas- sification result is evaluated by calculating inner products between the test vector and vectors from the training set (e,?), so the mapping used does not have to be speci- fied explicitly. Instead, only the rule for efficient calcula- tion of the inner product has to he known. Introduction of a kernel function (term that came from integral calculus) K ( K  2) = ($(?), @(2)) allows for the combination of im- plicit mapping and evaluation of a target function in one computation. Evaluation of the target function in the dual representa- tion of the linear case consists of calculation of the follow- ing sum: I f(2) = aiyi (&2) + b i=l  where 1 is a training set size, yi is the classification from the training set, b is a constant offset, and ai are the coefficients obtained from a training procedure. ai can be interpreted as ”an indication of the information content of the exam- ple E” [ I I]. Typically, most of ai coefficients are zeroes, and those vectors that have corresponding non-zero coeffi- cients, are called support vectors. They represent a subset 53 I of training set that is essential for the classification. A non- support vector can be removed from the training set without any consequences; removal of a support vector leads to a change in the decision surface. In a two-class problem, the sign of the target function f(?) gives an answer if the test vector f belongs to the class, or not. Value of f (? )  reflects the strength of the decision - distance of the vector from the decision surface. Another important strength of SVMs is its ability to al- low for outliers, which are always present in real data. In- troduction of margin slack variables & and the regularisa- tion parameter C gives the possibility to ”trade-off between complexity and losses” [ 1 I]. SVM classification can be formulated for the multi-class case, as the one considered in this paper. Usually one of two approaches is chosen: either one-against-one, which requires development of models (k is the number of classes in the training set), or more simple one-against- all, which requires k models. Vectors from the test set are tested using all the models and final decision is made using a voting scheme. In the case study three regions of seafloor were inter- temational Corporation (SATC) for data collection, and Lt. Shep Smith, NOAA, for bathymetric grid generation. 6. REFERENCES [I] V E Kostylev, B J Todd, G B Fader, R C Courtney, G D Cameron, R A  Pickrill, ”Benthic habitat mapping on the Scotian Shelf based on multibeam bathymetry, surficial geology and sea floor photographs.” Marine Ecology Progress Series, 219, 121-137,2001. [2] G R Cutter Jr, Y Rzhanov, L A Mayer, ”Automated segmentation of seafloor bathymetly from multibeam echosounder data using local Fourier histogram tex- ture features.” Journal of Experimental Marine Biol- ogy and Ecology, v. 4065, pp. 1-16,2002. [3] J J Koenderink, A J van Doom, ”Representation of local geometly in the visual system.” Biological Cy- bemetics, v.55, pp.367-375, 1987. [4] C Schmid, R Mohr, ”Matching by local invariants” INRIA Technical Report N 2644, 1995. preted as possessing distinct morphological character and sediment types, based upon core samples, and were used to determine areas for trainine data sets. Six models develoned [51 F zhOu, J Few,  Q Shi, ’Texture feature based on local Fourier transform:’ K I P  200 I Conference proceed- - ings, pp.610-613,2001. by SVMs trained on these sets were used for classification of the rest of the data. The results are in satisfactory agree- ment with those obtained by clustering techniques, as well as classification done manually [9]. [61 Pham, models for huy clustering? Computer Vision and Image Understanding, v.84, pp.285-297,2001, 4. CONCLUSIONS [7] F Murtagh, ”Multidimensional clustering algorithms.’’ Physica-Verlag, Heidelberg, 1985. IIA are descriptors of 2D raster data that can be used to char- acterize seafloor morphology. Results of this study show that distinct morphological regions of the seafloor were seg- mented by classifying feature vectors formed from IIA’s. The efficiency of the segmentation is generally high - good agreement exists with the manually delineated substrate map and the refined map based upon recent multibeam echosounder Minasny, A McBratney, .,FuzME 2, Australian Centre for Precision Agriculture, The Uni- versity of Sydney, NSW 2006. [93 L G Ward, ”Sedimentology of the lower Great Bayii’iscataqua River Estuary”, San Diego, C A  De- partment of the Navy, NCCOSC RDTE Division Re- port, 1995. bathymetry and backscatter data. Agreement between un- supervised IIA segmentation and LFH segmentation is also zenerallv good for the orimarv eeomomholo&al [IO] V N Vapnik ”Statistical learning theory.” 1998, John - . -  _ -  . I  - Supervised classification and segmentation according to re- sults of SVM’s allows for incorporation of ground truth data Cristianini, Shawe-Taylor, introduction to Support Vector Machines and other kernel-based (grab samples, underwater video) and is not affected by re- strictions imposed by clustering techniques. However to learning methods.” Cambridge University Press, 2000. distinguish many classes requires many models and adds complexity to the analysis. Wiley & Sons. 5. ACKNOWLEDGEMENTS This research was supported by NOAA grant NA970GO241. The authors would like to thank Science Applications In- 532 

Seafloor Segmentation Based on Bathymetric Measurements from Multibeam Echosounders Data

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Seafloor Segmentation Based on Bathymetric Measurements from Multibeam Echosounders Data

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