In this work, multibeam echosounder (MBES) and dual frequency sidescan sonar (SSS) data are combined to map the shallow (5–100 m) benthic habitats of the National Marine Park of Zakynthos (NMPZ), Greece, a Marine Protected Area (MPA). NMPZ hosts extensive prairies of the protected Mediterranean phanerogams Posidonia oceanica and Cymodocea nodosa, as well as reefs and sandbanks. Seafloor characterization is achieved using the multi-frequency acoustic backscatter of: (a) the two simultaneous frequencies of the SSS (100 and 400 kHz) and (b) the MBES (180 kHz), as well as the MBES bathymetry. Overall, these high-resolution datasets cover an area of 84 km² with ground coverage varying from 50% to 100%. Image texture, terrain and backscatter angular response analyses are applied to the above, to extract a range of statistical features. Those have different spatial densities and so they are combined through an object-based approach based on the full-coverage 100-kHz SSS mosaic. Supervised classification is applied to data models composed of operationally meaningful combinations between the above features, reflecting single-sonar or multi-sonar mapping scenarios. Classification results are validated against a detailed expert interpretation habitat map making use of extensive ground-truth data. The relative gain of one system or one feature extraction method or another are thoroughly examined. The frequency-dependent separation of benthic habitats showcases the potentials of multi-frequency backscatter and bathymetry from different sonars, improving evidence-based interpretations of shallow benthic habitats.

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.

Litter that accumulates in the marine environment is recognized as a global environmental problem with numerous adverse effects. Despite Greece having more than 16,000 km of coastline, studies on litter composition and sources on its beaches are scarce and fragmentary. Therefore, this study aimed to quantify beach-stranded litter by type of material (plastic, glass, paper, aluminum, other metals, building materials, rope) on 80 Greek beaches, and to explore the relationships between different litter materials and marine- and land-based sources. Field work and data collection were accomplished with the participation of 10,938 and 15,748 volunteers in 2006 and 2007, respectively, and were integrated in the nationwide campaign of voluntary beach clean-ups: “Clean up the Med.” The participation of thousands of volunteers of different ages and social backgrounds in the data collection process not only led to the realization of a national level survey, but also played a significant role in raising public awareness on marine environmental issues, and in promoting a sense of responsibility in protecting marine resources. Plastic was the most abundant litter material found on the surveyed beaches (43–51%), followed by paper (13–18%) and aluminum (7–12%). Onshore and nearshore recreational activities, such as bathing and recreational boating, appeared to be the most dominant litter sources, as they affect the majority (56) of the surveyed beaches, suggesting that environmental education and awareness could reduce the beach-litter pollution problem. Navigation seemed to be the dominant source of marine-based litter, affecting more than half (46) of the surveyed beaches.

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.