A Decision Support System to Ease Operator Overload in Multibeam Passive Sonar

Creating human-informative signal processing systems for the underwater acoustic environment that do not generate operator cognitive saturation and overload is a major challenge. To alleviate cognitive operator overload, we present a visual analytics methodology in which multiple beam-formed sonar returns are mapped to an optimized 2-D visual representation, which preserves the relevant data structure. This representation alerts the operator as to which beams are likely to contain anomalous information by modeling a latent distribution of information for each beam. Sonar operators therefore focus their attention only on the surprising events. In addition to the principled visualization of high-dimensional uncertain data, the system quantifies anomalous information using a Fisher Information measure. Central to this process is the novel use of both signal and noise observation modeling to characterize the sensor information. A demonstration of detecting exceptionally low signal-to-noise ratio targets embedded in real-world 33-beam passive sonar data is presented

Acoustic data optimisation for seabed mapping with visual and computational data mining

Oceans cover 70% of Earth’s surface but little is known about their waters. While the echosounders, often used for exploration of our oceans, have developed at a tremendous rate since the WWII, the methods used to analyse and interpret the data still remain the same. These methods are inefficient, time consuming, and often costly in dealing with the large data that modern echosounders produce. This PhD project will examine the complexity of the de facto seabed mapping technique by exploring and analysing acoustic data with a combination of data mining and visual analytic methods. First we test the redundancy issues in multibeam echosounder (MBES) data by using the component plane visualisation of a Self Organising Map (SOM). A total of 16 visual groups were identified among the 132 statistical data descriptors. The optimised MBES dataset had 35 attributes from 16 visual groups and represented a 73% reduction in data dimensionality. A combined Principal Component Analysis (PCA) + k-means was used to cluster both the datasets. The cluster results were visually compared as well as internally validated using four different internal validation methods. Next we tested two novel approaches in singlebeam echosounder (SBES) data processing and clustering – using visual exploration for outlier detection and direct clustering of time series echo returns. Visual exploration identified further outliers the automatic procedure was not able to find. The SBES data were then clustered directly. The internal validation indices suggested the optimal number of clusters to be three. This is consistent with the assumption that the SBES time series represented the subsurface classes of the seabed. Next the SBES data were joined with the corresponding MBES data based on identification of the closest locations between MBES and SBES. Two algorithms, PCA + k-means and fuzzy c-means were tested and results visualised. From visual comparison, the cluster boundary appeared to have better definitions when compared to the clustered MBES data only. The results seem to indicate that adding SBES did in fact improve the boundary definitions. Next the cluster results from the analysis chapters were validated against ground truth data using a confusion matrix and kappa coefficients. For MBES, the classes derived from optimised data yielded better accuracy compared to that of the original data. For SBES, direct clustering was able to provide a relatively reliable overview of the underlying classes in survey area. The combined MBES + SBES data provided by far the best accuracy for mapping with almost a 10% increase in overall accuracy compared to that of the original MBES data. The results proved to be promising in optimising the acoustic data and improving the quality of seabed mapping. Furthermore, these approaches have the potential of significant time and cost saving in the seabed mapping process. Finally some future directions are recommended for the findings of this research project with the consideration that this could contribute to further development of seabed mapping problems at mapping agencies worldwide

Comparative Characterization of Two Nearshore Rocky Reef Areas: A high-use recreational fishing reef vs. an unfished reef

This project has provided the ODFW Marine Habitat Project with a unique opportunity to make a comparative examination of the habitats and fish communities of a heavily fished reef off of Siletz Bay, and a nearly unfished reef off Cape Perpetua. There are over 17,000 angler trips for groundfish out of Depoe Bay annually, many choosing to fish Siletz Reef. In addition, numerous whale watching and sightseeing charter vessels operate in the vicinity of the reef. Cape Perpetua Reef consists of isolated patches of rock, and is too far from Newport (the nearest port) to receive much fishing effort. This examination of the influences of fishing pressure in nearshore rocky habitat complements, and is embedded in, a broader effort to understand the distribution and character of Oregon’s nearshore rocky reefs, and the species that are dependent upon them for habitat. The ODFW Marine Habitat Project has spent the last eight years working in cooperation with scientists and other resource agencies to develop methods for classifying and mapping nearshore rocky reefs habitats off Oregon. Because nearshore reefs are in state waters, Oregon is responsible for managing these living resources and habitats to sustain their long-term use and productivity. To date, eight reefs have been surveyed and mapped with sidescan and/or multibeam bathymetry at a resolution indicative of fish habitat. ODFW has also been developing non-extractive fish survey techniques to describe fish-habitat associations and estimate fish abundance at the habitat and reef scale. The importance of habitat to reef species cannot be over-stated and is now widely accepted in the scientific community and is the focus of major research efforts in other Pacific coastal states. Better understanding of the degree to which harvest influences relative and absolute abundance, combined with species-habitat relationships, is essential for resource managers to exercise their stewardship responsibilities to protect 5 essential habitat, develop population indices for species at risk, and maintain healthy and productive ecological systems

Benthic habitat mapping in coastal waters of south–east Australia

The Victorian Marine Mapping Project will improve knowledge on the location, spatial distribution, condition and extent of marine habitats and associated biodiversity in Victorian State waters. This information will guide informed decision making, enable priority setting, and assist in targeted natural resource management planning. This project entails benthic habitat mapping over 500 square kilometers of Victorian State waters using multibeam sonar, towed video and image classification techniques. Information collected includes seafloor topography, seafloor softness and hardness (reflectivity), and information on geology and benthic flora and fauna assemblages collectively comprising habitat. Computerized semi-automated classification techniques are also being developed to provide a cost effective approach to rapid mapping and assessment of coastal habitats.Habitat mapping is important for understanding and communicating the distribution of natural values within the marine environment. The coastal fringe of Victoria encompasses a rich and diverse ecosystem representative of coastal waters of South-east Australia. To date, extensive knowledge of these systems is limited due to the lack of available data. Knowledge of the distribution and extent of habitat is required to target management activities most effectively, and provide the basis to monitor and report on their status in the future.<br /

Probabilistic topographic information visualisation

The focus of this thesis is the extension of topographic visualisation mappings to allow for the incorporation of uncertainty. Few visualisation algorithms in the literature are capable of mapping uncertain data with fewer able to represent observation uncertainties in visualisations. As such, modifications are made to NeuroScale, Locally Linear Embedding, Isomap and Laplacian Eigenmaps to incorporate uncertainty in the observation and visualisation spaces. The proposed mappings are then called Normally-distributed NeuroScale (N-NS), T-distributed NeuroScale (T-NS), Probabilistic LLE (PLLE), Probabilistic Isomap (PIso) and Probabilistic Weighted Neighbourhood Mapping (PWNM). These algorithms generate a probabilistic visualisation space with each latent visualised point transformed to a multivariate Gaussian or T-distribution, using a feed-forward RBF network. Two types of uncertainty are then characterised dependent on the data and mapping procedure. Data dependent uncertainty is the inherent observation uncertainty. Whereas, mapping uncertainty is defined by the Fisher Information of a visualised distribution. This indicates how well the data has been interpolated, offering a level of ‘surprise’ for each observation. These new probabilistic mappings are tested on three datasets of vectorial observations and three datasets of real world time series observations for anomaly detection. In order to visualise the time series data, a method for analysing observed signals and noise distributions, Residual Modelling, is introduced. The performance of the new algorithms on the tested datasets is compared qualitatively with the latent space generated by the Gaussian Process Latent Variable Model (GPLVM). A quantitative comparison using existing evaluation measures from the literature allows performance of each mapping function to be compared. Finally, the mapping uncertainty measure is combined with NeuroScale to build a deep learning classifier, the Cascading RBF. This new structure is tested on the MNist dataset achieving world record performance whilst avoiding the flaws seen in other Deep Learning Machines

Biodiversity patterns in False Bay: an assessment using underwater cameras

Understanding how marine biodiversity is distributed, and what drives these patterns, relies on good descriptions of marine ecosystems. This information should inform the protection of biodiversity and guide its management. Relationships between marine landscapes and biodiversity therefore need to be described at scales that are useful to regional management. Simultaneous sampling of marine biodiversity and the seafloor is challenging, so baseline ecosystem descriptions are often mismatched in their abiotic and biotic components. Cameras can sample the seafloor and its associated biodiversity concurrently, with good coverage and at low cost. These are important considerations for sustainable monitoring to inform conservation management in resource-limited regions. Terrestrial landscape characterisations cannot simply be translated to the ocean because interpreting remote ocean terrain assessments in a manner relevant to ecological analysis is complicated by depth, circulation, light attenuation, and other oceanographic variables. The integration of some of these concepts into rapid marine biodiversity assessments therefore needs ground-truthing where they are applied in new regions, to advance sustainability in long-term marine monitoring. This thesis investigated the relationship between landscape composition and benthic marine biodiversity in False Bay, South Africa using novel methods that extended biodiversity sampling across more depths and habitats than any single, previous survey of the bay. This study's approach piloted sampling and interpreting the marine landscape and biodiversity over matching spatial and temporal scales. The coverage, repeatability and ecosystem-level scale applied to this study make it a useful basis to develop monitoring protocols that are appropriate to conservation management at relevant regional scales. New insights for the region include a) a new description of the seafloor using classifications that explain the variation in epibenthic megafauna and ichthyofauna communities, b) a quantitative account of the epibenthic megafauna on the eastern reefs where species diversity was highest, and c) the synthesis of seafloor descriptions with the epibenthic megafauna and ichthyofauna to describe nine regions of False Bay, relative to two previous descriptions of "grounds". Photographs and multibeam bathymetry characterised the seafloor on eight transects across the bay and were ground-truthed by grab samples repeated at representative sites. Two new classifications were applied to describe the seafloor. Horizontal seafloor heterogeneity was highest in the east, and reef was distributed along the eastern and western margins. The Collaborative and Automated Tools for Analysis of Marine Imagery (CATAMI) scheme captured accurate broad-scale descriptions of the physical landscape when applied to photographs. Grabs are still needed to provide fine-scale particle size data on soft sediments where most invertebrate diversity is likely infauna. However, CATAMI abstracts fine-scale sediment variation into simpler groupings more useful for rapid ecosystem assessment. Photographic sampling is repeatable, which is useful for long-term ecosystem monitoring. Photographs taken using a jump camera rig assessed the epibenthic megafauna across habitats and along depth gradients. Rényi diversity showed that species diversity increased in shallow waters up to 40 m, reaching a peak between 30 and 40 m, before decreasing with increasing depth. Species diversity was highest in the east, where seafloor heterogeneity was also highest. This result is interesting because eastern False Bay falls mostly outside the current marine protected area (MPA) network and has been relatively under-represented in previous surveys. The jump camera documents ecosystem-level biodiversity patterns and processes, and the random point count method in Coral Point Count (CPCe) is useful to assess community composition and cover on reefs. The relative abundance and distribution of ichthyofauna were assessed using baited remote underwater video systems (BRUVs). Fifty-seven fish species from 30 families were recorded between 4 and 84 m. Rényi diversity showed that species richness was similar for reef and sand overall, but the Shannon-Wiener diversity index (H') was significantly higher on reef sites than on sand sites (t = 1.972, p < 0.0001). Species richness for the whole bay was similar in winter and summer, which indicates that the same species are likely present year-round; however, the Shannon-Wiener diversity index was significantly higher in winter (t = 1.973, p < 0.013) and evenness was greater in winter at the level of the site. These findings highlight the difficulty in protecting sufficient sand habitat to encompass the patchy distribution of sand-associated species and highlight seasonal differences in optimal visibility for future camera monitoring surveys by conservation management. There are clear patterns in the marine biodiversity of False Bay, at various scales, that can be detected using novel methods for the region. The study's approach to classifying both the landscape and its associated biodiversity creates a framework for future ecosystem threat assessment that can be applied elsewhere, especially along the South African coastline

Machine learning methods for discriminating natural targets in seabed imagery

The research in this thesis concerns feature-based machine learning processes and methods for discriminating qualitative natural targets in seabed imagery. The applications considered, typically involve time-consuming manual processing stages in an industrial setting. An aim of the research is to facilitate a means of assisting human analysts by expediting the tedious interpretative tasks, using machine methods. Some novel approaches are devised and investigated for solving the application problems. These investigations are compartmentalised in four coherent case studies linked by common underlying technical themes and methods. The first study addresses pockmark discrimination in a digital bathymetry model. Manual identification and mapping of even a relatively small number of these landform objects is an expensive process. A novel, supervised machine learning approach to automating the task is presented. The process maps the boundaries of ≈ 2000 pockmarks in seconds - a task that would take days for a human analyst to complete. The second case study investigates different feature creation methods for automatically discriminating sidescan sonar image textures characteristic of Sabellaria spinulosa colonisation. Results from a comparison of several textural feature creation methods on sonar waterfall imagery show that Gabor filter banks yield some of the best results. A further empirical investigation into the filter bank features created on sonar mosaic imagery leads to the identification of a useful configuration and filter parameter ranges for discriminating the target textures in the imagery. Feature saliency estimation is a vital stage in the machine process. Case study three concerns distance measures for the evaluation and ranking of features on sonar imagery. Two novel consensus methods for creating a more robust ranking are proposed. Experimental results show that the consensus methods can improve robustness over a range of feature parameterisations and various seabed texture classification tasks. The final case study is more qualitative in nature and brings together a number of ideas, applied to the classification of target regions in real-world sonar mosaic imagery. A number of technical challenges arose and these were surmounted by devising a novel, hybrid unsupervised method. This fully automated machine approach was compared with a supervised approach in an application to the problem of image-based sediment type discrimination. The hybrid unsupervised method produces a plausible class map in a few minutes of processing time. It is concluded that the versatile, novel process should be generalisable to the discrimination of other subjective natural targets in real-world seabed imagery, such as Sabellaria textures and pockmarks (with appropriate features and feature tuning.) Further, the full automation of pockmark and Sabellaria discrimination is feasible within this framework