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
. 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.