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In the present paper, a methodological scheme, bringing together common Acoustic Seabed Classification (ASC) systems and a powerful data decomposition approach, called Independent Component Analysis (ICA), is demonstrated regarding its suitability for detecting small targets in Side Scan Sonar imagery. Traditional ASC systems extract numerous texture descriptors, leading to a large feature vector, the dimensionality of which is reduced by means of data decomposition techniques, usually Principal Component Analysis (PCA), prior to classification. However, in the target detection issue, data decomposition should point towards finding components that represent sub-ordinary image information (i.e., small targets) rather than a dominant one. ICA has long been proved to be suitable for separating targets from a background, and this study represents a novel exhibition of its applicability to Side Scan Sonar (SSS) images. The present study attempts to build a fully automated target detection approach that combines image based feature extraction, ICA, and unsupervised classification. The suitability of the proposed approach has been demonstrated using an SSS data-set containing more than 70 manmade targets, most of them metallic, validated through a marine magnetic survey or ground truthing inspection. The method exhibited very good performance as it was able to detect more than 77% of the targets and it produced less than seven false alarms per km². Moreover, it was compared to cases where, in the exact same methodological scheme, no decomposition technique is used, or PCA is employed instead of ICA, achieving the highest detection rate, but, more importantly, producing more than six times less false alarms, thus proving that ICA successfully manages to maximize target to background separation. [ABSTRACT FROM AUTHOR]

In many remote-sensing applications, measured data are a strong function of the environment in which they are collected. This paper introduces a new context-dependent classification algorithm to address and exploit this phenomenon. Within the proposed framework, an ensemble of classifiers is constructed, each associated with a particular environment. The key to the method is that the relative importance of each object (i.e., data point) during the learning phase for a given classifier is controlled via a modulating factor based on the similarity of auxiliary environment features. Importantly, the number of classifiers to learn and all other associated model parameters are inferred automatically from the training data. The promise of the proposed method is demonstrated on classification tasks seeking to distinguish underwater targets from clutter in synthetic aperture sonar imagery. The measured data were collected with an autonomous underwater vehicle during several large experiments, conducted at sea between 2008 and 2012, in different geographical locations with diverse environmental conditions. For these data, the environment was quantified by features (extracted from the imagery directly) measuring the anisotropy and the complexity of the seabed. Experimental results suggest that the classification performance of the proposed approach compares favorably to conventional classification algorithms as well as state-of-the-art context-dependent methods. Results also reveal the object features that are salient for performing target classification in different underwater environments. [ABSTRACT FROM PUBLISHER]

Marine geophysical data collected during an underwater natural and cultural heritage assessment survey along the coastal zone of Kefallinia Island in the Ionian Sea, Greece, showed among other seafloor features, the presence of a Roman shipwreck and its amphorae cargo on the seafloor. The study and analysis of the collected data demonstrated that: (i) sidescan sonar and chirp sub-bottom profiling systems, can successfully detect ancient shipwrecks and their amphorae cargo on the seafloor, (ii) the use of objective computer vision techniques in processing sidescan sonar seafloor images, is a valuable tool for the separation of potential ancient shipwreck targets from other seafloor features with similar acoustic signatures. Furthermore, a guideline for the data acquisition parameters that should be used to obtain optimal seafloor sonar images to maximize the separation of potential shipwreck targets from other seafloor features, is provided. The underwater sonar remote sensing techniques may also provide adequate indication regarding the amphorae hull stowage and its vulnerability to human activity in the area. The shipwreck is dated between 1st century BC and 1st century AD and is one of the largest found so far in the Mediterranean Sea, for that period. It is estimated that it was carrying about 6,000 amphorae. The amphorae cargo, visible on the seafloor, is in very good state of preservation and the shipwreck has the potential to yield a wealth of information about the shipping routes, trading, amphorae hull stowage and ship construction during the relevant period and is therefore considered to be of significant archaeological importance. • Ancient shipwreck underwater sonar remote sensing detection. • Automated target detection. • Acquisition parameters to obtain seafloor images to maximize the separation of wreck targets among other seafloor features. • Roman wrecks 1st century BC to 1st century AD, Mediterranean Sea. [ABSTRACT FROM AUTHOR]

Bottom trawl footprints are a prominent environmental impact of deep-sea fishery that was revealed through the evolution of underwater remote sensing technologies. Image processing techniques have been widely applied in acoustic remote sensing, but accurate trawl-mark (TM) detection is underdeveloped. The paper presents a new algorithm for the automatic detection and spatial quantification of TMs that is implemented on sidescan sonar (SSS) images of a fishing ground from the Gulf of Patras in the Eastern Mediterranean Sea. This method inspects any structure of the local seafloor in an environmentally adaptive procedure, in order to overcome the predicament of analyzing noisy and complex SSS images of the seafloor. The initial preprocessing stage deals with radiometric inconsistencies. Then, multiplex filters in the spatial domain are performed with multiscale rotated Haar-like features through integral images that locate the TM-like forms and additionally discriminate the textural characteristics of the seafloor. The final TMs are selected according to their geometric and background environment features, and the algorithm successfully produces a set of trawling-ground quantification values that could be established as a baseline measure for the status assessment of a fishing ground. [ABSTRACT FROM AUTHOR]