Tuesday, June 14, 2011

Detecting reefs with ANN

I have just published a paper, along with several of my colleagues at the University of Adelaide, on detecting reefs using MLP.

The problem was that while there is coarse-scale bathymetric data from sonar surveys, and surveys of small areas that list the presence and absence of reefs in a relatively small number of points, there have not been large-scale surveys of where, exactly, reefs are. This is because the fine-scale sonar surveys needed to detect them remotely are very expensive and time consuming, and surveying manually (divers going into the water and looking) can be dangerous in places (either dangerous sea conditions, or big bitey beasties in the water). Not knowing where reefs are is a problem, especially if you want to construct ecological models of reef-dwelling creatures like abalone. In short, abalone like to live on reefs, so to build an accurate model, you must know where the reefs are.

We addressed this problem by firstly, processing the bathymetric data into slope and curvature measures of the sea bed, then training MLP over sliding 2D windows of these variables, where a known reef presence or absence was in the centre of the window. A window in this case was an n * n matrix of values, where we used n=5. So, the third element of the third row was the target cell, which the MLP was learning to classify as either a reef or non-reef point.

We found that combinations of the bathymetric value of the target cell, and a 5*5 window of seabed slope, gave us the best results. The overall experimental method we used was as I described in this post. While we weren't able to classify every reef exactly, the overall accuracy of 85% was enough to construct a useful map of reefs for ecological models of abalone.

We're looking at boosting the accuracy of our models by various means - this first paper is just a proof-of-concept, to show that we can find reefs with ANN.

The full citation for this paper is:

Watts, M.J., et al., A novel method for mapping reefs and subtidal rocky habitats using artificial neural networks. Ecological Modelling (2011), doi:10.1016/j.ecolmodel.2011.04.024