Automated interpretation of benthic stereo imagery

Autonomous benthic imaging, reduces human risk and increases the amount of collected data. However, manually interpreting these high volumes of data is onerous, time consuming and in many cases, infeasible. The objective of this thesis is to improve the scientific utility of the large image datasets. Fine-scale terrain complexity is typically quantified by rugosity and measured by divers using chains and tape measures. This thesis proposes a new technique for measuring terrain complexity from 3D stereo image reconstructions, which is non-contact and can be calculated at multiple scales over large spatial extents. Using robots, terrain complexity can be measured without endangering humans, beyond scuba depths. Results show that this approach is more robust, flexible and easily repeatable than traditional methods. These proposed terrain complexity features are combined with visual colour and texture descriptors and applied to classifying imagery. New multi-dataset feature selection methods are proposed for performing feature selection across multiple datasets, and are shown to improve the overall classification performance. The results show that the most informative predictors of benthic habitat types are the new terrain complexity measurements. This thesis presents a method that aims to reduce human labelling effort, while maximising classification performance by combining pre-clustering with active learning. The results support that utilising the structure of the unlabelled data in conjunction with uncertainty sampling can significantly reduce the number of labels required for a given level of accuracy. Typically 0.00001–0.00007% of image data is annotated and processed for science purposes (20–50 points in 1–2% of the images). This thesis proposes a framework that uses existing human-annotated point labels to train a superpixel-based automated classification system, which can extrapolate the classified results to every pixel across all the images of an entire survey

Biometric assessment of deep-sea vent megabenthic communities using multi-resolution 3D image reconstructions

This paper describes a method to survey the distribution of megabenthos over multi-hectare regions of the seafloor. Quantitative biomass estimates are made by combining high-resolution 3D image reconstructions, used to model spatial relationships between representative taxa, with lower-resolution reconstructions taken over a wider area in which the distribution of larger predatory animals can be observed. The method is applied to a region of the Iheya North field that was the target of scientific drilling during the IODP Expedition 331 in 2010. An area of 2.5 ha was surveyed 3 years and 4 months after the site was drilled. More than 100,000 organisms from 6 taxa were identified. The visible effects of drilling on the distribution of megabenthos were confined to a 20 m radius of the artificially created hydrothermal discharges, with the associated densities of biomass lower than observed in nearby naturally discharging areas

Multi-scale measures of rugosity, slope and aspect from benthic stereo image reconstructions.

This paper demonstrates how multi-scale measures of rugosity, slope and aspect can be derived from fine-scale bathymetric reconstructions created from geo-referenced stereo imagery. We generate three-dimensional reconstructions over large spatial scales using data collected by Autonomous Underwater Vehicles (AUVs), Remotely Operated Vehicles (ROVs), manned submersibles and diver-held imaging systems. We propose a new method for calculating rugosity in a Delaunay triangulated surface mesh by projecting areas onto the plane of best fit using Principal Component Analysis (PCA). Slope and aspect can be calculated with very little extra effort, and fitting a plane serves to decouple rugosity from slope. We compare the results of the virtual terrain complexity calculations with experimental results using conventional in-situ measurement methods. We show that performing calculations over a digital terrain reconstruction is more flexible, robust and easily repeatable. In addition, the method is non-contact and provides much less environmental impact compared to traditional survey techniques. For diver-based surveys, the time underwater needed to collect rugosity data is significantly reduced and, being a technique based on images, it is possible to use robotic platforms that can operate beyond diver depths. Measurements can be calculated exhaustively at multiple scales for surveys with tens of thousands of images covering thousands of square metres. The technique is demonstrated on data gathered by a diver-rig and an AUV, on small single-transect surveys and on a larger, dense survey that covers over [Formula: see text]. Stereo images provide 3D structure as well as visual appearance, which could potentially feed into automated classification techniques. Our multi-scale rugosity, slope and aspect measures have already been adopted in a number of marine science studies. This paper presents a detailed description of the method and thoroughly validates it against traditional in-situ measurements

A Simple, fast, and repeatable survey method for underwater visual 3D benthic mapping and monitoring

Visual 3D reconstruction techniques provide rich ecological and habitat structural information from underwater imagery. However, an unaided swimmer or diver struggles to navigate precisely over larger extents with consistent image overlap needed for visual reconstruction. While underwater robots have demonstrated systematic coverage of areas much larger than the footprint of a single image, access to suitable robotic systems is limited and requires specialized operators. Furthermore, robots are poor at navigating hydrodynamic habitats such as shallow coral reefs. We present a simple approach that constrains the motion of a swimmer using a line unwinding from a fixed central drum. The resulting motion is the involute of a circle, a spiral-like path with constant spacing between revolutions. We test this survey method at a broad range of habitats and hydrodynamic conditions encircling Lizard Island in the Great Barrier Reef, Australia. The approach generates fast, structured, repeatable, and large-extent surveys (~110 m² in 15 min) that can be performed with two people and are superior to the commonly used “mow the lawn” method. The amount of image overlap is a design parameter, allowing for surveys that can then be reliably used in an automated processing pipeline to generate 3D reconstructions, orthographically projected mosaics, and structural complexity indices. The individual images or full mosaics can also be labeled for benthic diversity and cover estimates. The survey method we present can serve as a standard approach to repeatedly collecting underwater imagery for high-resolution 2D mosaics and 3D reconstructions covering spatial extents much larger than a single image footprint without requiring sophisticated robotic systems or lengthy deployment of visual guides. As such, it opens up cost-effective novel observations to inform studies relating habitat structure to ecological processes and biodiversity at scales and spatial resolutions not readily available previously.13 page(s

Dense AUV grid at Scott Reef off western Australia covering with 9,831 stereo image pairs.

<p>(A) Textured 3D mesh overview of survey site reconstructed using the method outlined in sec:data. (B) Close up of transition zone showing dense coral cover, barren sand and an intermediate, partially populated substrate class. (C) Colour map of mesh depth/bathymetry.</p

High-resolution underwater robotic vision-based mapping and three-dimensional reconstruction for archaeology

Documenting underwater archaeological sites is an extremely challenging problem. Sites covering large areas are particularly daunting for traditional techniques. In this paper, we present a novel approach to this problem using both an autonomous underwater vehicle (AUV) and a diver-controlled stereo imaging platform to document the submerged Bronze Age city at Pavlopetri, Greece. The result is a three-dimensional (3D) reconstruction covering 26,600 m2 at a resolution of 2 mm/pixel, the largest-scale underwater optical 3D map, at such a resolution, in the world to date. We discuss the advances necessary to achieve this result, including i) an approach to color correct large numbers of images at varying altitudes and over varying bottom types; ii) a large-scale bundle adjustment framework that is capable of handling upward of 400,000 stereo images; and iii) a novel approach to the registration and rapid documentation of an underwater excavations area that can quickly produce maps of site change. We present visual and quantitative comparisons to the authors' previous underwater mapping approaches

Results for simulated terrain model for exponential function.

<p>, where , and are Depth, Northing and Easting in metres. The results are computed with a mesh resolution of and a window size of . (A) shows an oblique view of the 3D bathymetry, (B) shows the slope angle, (C) shows the rugosity projected onto the N-E horizontal plane and (D) shows the rugosity projected onto the plane of best fit.</p