Robust morphological detection of sea mines in side-scan sonar images

Abstract

The automated detection of sea mines remains an increasingly important humanitarian and military task. In recent years, research efforts have been concentrated on developing algorithms that detect mines in complicated littoral environments. Acquired high-resolution side-looking sonar images are often heavily infested with artifacts from natural and man-made clutter. As a consequence, automated detection algorithms, designed for high probability of detection, suffer from a large number of false alarms. To remedy this situation, sophisticated feature extraction and pattern classification techniques are commonly used after detection.In this paper, we propose a nonlinear detection algorithm, based on mathematical morphology, for the robust detection of sea mines. The proposed algorithm is fast and performs well under a variety of sonar modalities and operating conditions. Our approach is based on enhancing potential mine signatures by extracting highlight peaks of appropriate shape and size and by boosting the amplitude of the peaks associated with a potential shadow prior to detection. Signal amplitudes over highlight peaks are extracted using a flat morphological top-hat by reconstruction operator. The contribution of a potential shadow to the detection image is incorporated by increasing the associated highlight amplitude by an amount proportional to the relative contrast between highlight and shadow signatures. The detection image is then thresholded at mid-gray level. The largest p targets from the resulting binary image are then labelled as potential targets. The number of false alarms in the detection image is subsequently reduced to an acceptable level by a feature extraction and classification module.The detection algorithm is tested on two side-scan sonar databases provided by the Coastal Systems Station, Panama City, Florida: SONAR-0 and SONAR-3.

Mean–Standard Deviation Representation of Sonar Images for Echo Detection: Application to SAS Images

“…With being the number of realizations from 1 to and being the value for of the cumulative distribution function associated with , the Kolmogorov distance is defined as (12) The parameters of the Rayleigh and the Weibull laws are evaluated on the SAS image using a maximum-likelihood (ML) estimator. The estimated parameter of the Rayleigh law (2) is given by the following [23]: (13) where is the number of pixels and is the amplitude of pixel . Parameters and of the Weibull law (5) are estimated by and , respectively, given by the following [16]:…”

“…may be used. Operators derived from mathematical morphology have also been successfully designed [13]. In the context of underwater mine hunting, former studies investigated image segmentation and detailed analysis of projected shadows using a Markovian model of the sonar image [14], [15].…”

Hardware efficient underwater mine detection and classification

“…The so-called Alternating Sequential Filter (ASF) was applied on available images for noise reduction followed by top-hat (white) and anti-top-hat transforms (black) for detecting mine-bodies and mine-shadows, respectively. Batman and Goutsias [3] presented an unsupervised detection of sea mines using mathematical morphology. They laid emphasis on the simplicity, efficiency and speed of the design with a 0.95 probability of detection.…”

“…As suggested in previous work [3,5], statistical texture features are used to represent the data obtained from side-scan sonar. Two first order statistical features, mean and variance are found to be sufficient to represent the three classes: mine, shadow and background.…”

False alarm reduction by the And-ing of multiple multivariate Gaussian classifiers

“…This is especially true in difficult shallow water and very shallow water environments where man-made, biologic, and natural clutter give rise to many false alarms. Consequently, there has been much research devoted to development of robust algorithms for detection and classification of sea mines [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18].…”