Fuzzy c-means image segmentation of side-scan sonar images

Synthetic aperture side-scan sonar (SAS) is an imaging modality for detecting objects on the sea floor and in shallow water. SAS images provide an echo of an object along with its acoustic shadow; both of which can be used in the classification and localization of the object. We developed a Fuzzy C-Means (FCM) image segmentation algorithm that segments the echo of an object and its acoustic shadow in the presence of reverberation noise. INTRODUCTION In this paper we describe an image segmentation algorithm that was designed to segment a side-scan sonar image of the acoustic shadow of an object and the seabottom reverberation. This algorithm was developed with an additional constraint that no user input be required. A number of unsupervised techniques have been proposed to deal with segmentation of sonar images, including use of Markov Random Fields (MRF) to approximate the differing gray level regions and the pixel correlations within the regions [1, 2]. The latter technique provides an accurate segmentation procedure, but is computationally intense. When multiple images are available, a method of combining MRF clustering with image fusion has been developed [3]. A self-organizing feature map method [4] also has been effectively applied, but again involves iterative, computationally expensive processing. These methods ignored the object’s echo that can be used to better localize (e.g., height above the sea floor) the object during subsequent processing. Finally, in parallel with the development of the FCM procedure described here, we are developing an unsupervised split-and-merge algorithm that is capable of segmenting the acoustic shadow from the reverberation [5]. The purpose of an image-segmentation algorithm is to partition an image into its component regions (i.e., objects and background) [6, 7]. The K-Means or Hard C-Means algorithm (HCM) is an example of an unsupervised clustering algorithm [8] and has been shown to be a computationally efficient image segmentation procedure [9]. The segmentation results of the HCM algorithm are highly dependant upon the initialization resulting in highly variable segmentation due to the local minima of the associated objective function. This problem was over come by generalizing the HCM to allow for fuzzy membership function for each cluster resulting in the FCM algorithm [9, 10]. The FCM has been used to implement the image segmentation procedure [1012]. The FCM algorithm has been employed in conjunction with multiresolution processing techniques [13] and to solve the problem of shading effects during the segmentation of magnetic resonance images [14]. Both implementations deal with estimating the number of classes that are present, an approach that increases the computational complexity of the procedures. In this application, the procedure is required to estimate two clusters (i.e., acoustic-shadow and reverberation). In our application, image-segmentation serves as a preprocessing step for an object classification procedure, which is also under development. The segmentation algorithm should improve the image SNR prior to the classification module to minimize the likelihood of misclassification, thus maximizing the classifier’s efficacy. An additional benefit of the segmentation module is a reduced computational complexity required by the classification module because this procedure extracts out a subimage containing the region of interest and discards the bulk of the pixels that contain no information. Figure 1 – The top row contains the unprocessed SAS images of a cylinder (left) and a spheroid (right). Both objects show the brighter echo of the object itself and larger dark acoustic shadow. The bottom row shows the corresponding images following the preprocessing step. Throughout the paper, the gray scale (256 gray levels) of all of the images has been normalized to the range [0, 1]. Figure 1 shows the Synthetic Aperture Sonar (SAS) images of two objects. The images were simulated using the Shallow Water Acoustics Toolkit (SWAT), which is available from the Office of Naval Research [15]. All figures are plotted using a linear gray scale with 256 gray levels. The gray scale was normalized to the range [0, 1]. Both images were preprocessed to enhance the gray-scale contrast. The image-segmentation algorithm must be capable of dealing with variability in gray level range from one image to the next as well as the low signal-to-noise ratio (SNR) that are inherent to sonar images. In each case, the simulated range was 20-m from the platform and the object was floating 0.3-m above a coarse sand bottom. Our simulations employed the default horizontal array setting.